So, when we just fed the image to the network, a lot of detail got lost. Files: zip (5.9 MB) If you use this dataset please cite: Small Instance Detection by Integer Programming on Object Density Maps. The SSD ResNet FPN³ object detection model is used with a resolution of 640x640. As you can see in Picture 2, it worked quite good and provided a significant boost in accuracy. However it is very natural to create a custom dataset of your choice for object detection tasks. Each downsampling causes the image to be reduced by half. Small Object Detection in Optical Remote Sensing Images via Modified Faster R-CNN. As a result, the state-of-the-art object detection algorithm renders unsatisfactory performance as applied to detect small objects in images. For future work we believe that investigating more sophisticated techniques for improving the accuracy of small object detection, including the Generative Adversarial Nets, will be beneficial. Finally, in order to get the precise object bounding box, RCNN accurately locate and merge the foreground objects by regression operation. The problem with simply making the image larger using interpolation lies in that instead of 5×5 blurry pixels we will just get 10×10 (or 20×20, or whatever the multiplication factor you set) even blurrier pixels. The paper is organized as follows. Approaches described above are good, but far from the best, you will most likely get better results if you use the architectures that were specifically designed to find small objects. The results obtained are shown in Table 5. Some of the current detectors use so-called “anchors” to detect objects. But it requires an additional expense on storage space and time because RCNN needs to extract the features of 2000 proposal regions in each image. If you have a lot of classes to detect, one of the easiest ways to improve the detection of small objects and just classes that are hard to detect is using focal loss in the process of training a neural network. But just you wait, authors of this approach have done a pretty clever thing. Notably, blood cell detection is not a capability available in Detectron2 - we need to train the underlying networks to fit our custom task. The RCNN model proposed by Girshick in 2014 is divided into four processes during the object detection. It’s easy to see on the graph provided in the main paper itself: We have personally encountered a problem with models not detecting relatively small objects. We will dive deeper into how we solved it a bit later. In addition, it has been proved that the detection precision will be reduced if the negative sample is very high in the dataset. I highly suggest you read it in its entirety, but we’ll sum things up here: Since on pretty much all graphs you see about the speed/accuracy comparisons among the networks, the F-RCNN is always seen at the top-right corner, people have been trying really hard to improve both the speed and accuracy of this architecture. CNN works great for Image Recognition and there are many different architectures such as Yolo, Faster R-CNN, RetinaNet. So today we are going to talk about why do most popular object detection models are not that good at detecting small objects, how we can improve their performance and what are other known approaches to the problem. I'd like to use the Tensorflow Object Detection API to identify objects in a series of webcam images. Here you will find a list of ways to improve the detection of the YOLO architectures. Just like they have been doing in CSI forever now. The first criterion is that the actual size of the detected object is not more than 30 centimeters. 61662033 and 61473144, Aeronautical Science Foundation of China (Key Laboratory) under Grant no. Faster-RCNN integrates the generation of proposal region, extracting feature of proposal region, detection of bounding box, and classification of object into a CNN framework by the RPN network (region proposal network). In this paper, we dedicate an effort to bridge the gap. In this paper, we propose Comparison detector which still maintains the end-to-end fashion in training and … However, Fast-RCNN still needs to extract the proposal regions which is the same as RCNN. Faster-RCNN performs multiple downsampling operations in the process of feature extraction. But all of these objects in the image are large objects. In addition, the PASCAL VOC dataset is the main dataset for object detection and it is composed of 20 categories of object, e.g., cattle, buses, and pedestrians. While scale-level corresponding detection in feature pyramid network alleviates this problem, we find feature coupling of various scales still impairs the performance of small objects. No matter which way to carry out the object detection, the feature extraction uses multilayer convolution method, which can obtain the rich abstract object feature for the target object. However, because of the diversity of the detected objects, the current model fails to detect objects. However, the default setting is to resize the image into 300 x 300 (image_resizer). The authors declare that they have no conflicts of interest. As you can see, this network has a number of combinations of convolutions followed by a pooling layer. Firstly, they have been testing different pretrainined backbone networks to use in the F-RCNN for small object detection. However, we inevitably just get a small one in some application scenarios, especially medicine. Experiments show that the VGG-16 model takes only 0.2 seconds to detect each image. 2016YFF0103702. Then we repeat the above steps to get the final detection model. The paper compares our model with the state-of-the-art detection model Faster-RCNN for small object detection. For example, the Harr feature combined with Adaboosting classifier [14] is availability for face detection. Also, as well as in the previous paper about finding tiny faces, it was shown that using context around the objects significantly helps in detection. Because the big detected objects have many pixels in the image, they can accurately locate their location. So, let’s get to it. Sun, “Spatial pyramid pooling in deep convolutional networks for visual recognition,”, J. F. Dai, Y. Li, K. M. He et al., “R-FCN: Object Detection via Region-based Fully,” in, M. Everingham, L. van Gool, C. K. I. Williams, J. Winn, and A. Zisserman, “The pascal visual object classes (VOC) challenge,”, Y. Ren, C. Zhu, and S. Xiao, “Small object detection in optical remote sensing images via modified faster R-CNN,”, L. Itti, C. Koch, and E. Niebur, “A model of saliency-based visual attention for rapid scene analysis,”, M.-M. Cheng, N. J. Mitra, X. Huang, P. H. S. Torr, and S.-M. Hu, “Global contrast based salient region detection,”, C. Chen, M. Y. Liu, O. Tuzel et al., “R-CNN for small object detection,” in, J. S. Lim and W. H. Kim, “Detection of multiple humans using motion information and adaboost algorithm based on Harr-like features,”, R. P.Yadav, V. Senthamilarasu, K. Kutty, and S. P. Ugale, “Implementation of Robust HOG-SVM based Pedestrian Classification,”, L. Hou, W. Wan, K.-H. Lee, J.-N. Hwang, G. Okopal, and J. Pitton, “Robust Human Tracking Based on DPM Constrained Multiple-Kernel from a Moving Camera,”, A. Ali and M. A. Bayoumi, “Towards real-time DPM object detector for driver assistance,” in, S. Bell, C. L. Zitnick, K. Bala, and R. Girshick, “Inside-outside net: Detecting objects in context with skip pooling and recurrent neural networks,” in, T. Kong, A. Yao, Y. Chen, and F. Sun, “HyperNet: towards accurate region proposal generation and joint object detection,” in, F. Yang, W. Choi, and Y. Lin, “Exploit all the layers: fast and accurate CNN object detector with scale dependent pooling and cascaded rejection classifiers,” in, J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You only look once: Unified, real-time object detection,” in, W. Liu, D. Anguelov, D. Erhan et al., “SSD: single shot multibox detector,” in, J. R. R. Uijlings, K. E. A. The main reason is that those models based on deep neural network make the image calculated with convolution and downsampled in order to obtain more abstract and high-level features. As the mouse in Figure 1 is often placed next to the monitor, the common saliency detection model [11, 12] usually focuses on more significant monitor and ignores the mouse. The main process of training is shown in Table 1. Third, the objects are classified according to the features. To prevent such large-scale features from covering small scale features, the feature tensor that is outputted from different RoI pooling should be normalized before those tensors are concatenated. But instead of iteratively combining layers, they concatenate them and run a 1×1 convolution on the result. The author also gives the mAP of RCNN based on the dataset and it has only 23.5% detection rate. Indeed, one specificity of ATR is to require the detection of small targets while these dataset includes objects whose size in images is usually bigger than 200 pixels and can be the main topic of the image. Take a look, Small Object Detection in Optical Remote Sensing Images via Modified Faster, Small Object Detection with Multiscale Features, First Chinese Sample-Return Lunar Mission, Building a real-time, interactive video editing tool with machine learning. Each sampling causes the image to be reduced by half. obstacles. Preprocess the original MNIST images. a year ago. The Faster RCNN models pre-trained on the COCO dataset appear to be suitable, as they contain all the object categories I need.. We are mostly interested in the Hidden layers p… Some examples of labels missing from the original dataset: Stats. And the other is small objects; those are large objects in the real world, but they are shown in the image as small objects because of the camera angle and focal length, such as objects detection in aerial images or in remote sensing images. The ball, on the other hand, was still a problem though. Lin, M. Maire, S. Belongie et al., “Microsoft COCO: Common objects in context,” in, T. H. N. Le, Y. Zheng, C. Zhu, K. Luu, and M. Savvides, “Multiple scale faster-RCNN approach to driver’s cell-phone usage and hands on steering wheel detection,” in, X. Wu, K. Xu, and P. Hall, “A survey of image synthesis and editing with generative adversarial networks,”. All modern object detection algorithms are based on Convolutional Neural Networks. ZF net that has 5 convolutional layers and 3 fully connected layers is small network and the VGG_CNN_M_1024 is medium-sized network. The network is based on the Faster-RCNN detection model. The part renderings of the objects detection are shown in Figure 6. The 358 mouse are distributed in 282 images, and the other objects, e.g., toilet paper, faucet, socket panel, and clock, are shown in Table 3. From the personally served ads and movie recommendations to self-driving cars and automated food delivery services. 20162852031, and the Special Scientific Instrument Development of Ministry of Science and Technology of China under Grant no. In addition, we not only find the detected objects in the image but also need to accurately mark object location for object detection. Since 2014, Hinton used deep learning to achieve the best classification accuracy in the year's ImageNet competition, and then the deep learning has become a hot direction to detect the objects. Author(s): Balakrishnakumar V Step by step instructions to train Yolo-v5 & do Inference(from ultralytics) to count the blood cells and localize them.. Step 2 Using the trained RPN network in step 1, we generate the ROIs information and the probability distribution of the foreground objects in the proposal regions. The SUN dataset [27] consists of 908 scene categories and 4479 object categories and a total of 131067 images that also contain a large number of small objects. Through testing, the detection accuracy of our model for small objects is 11% higher than the state-of-the-art models. In this paper, the authors have done several things. This simple yet effective method showed to increase the overall Average Precision on Object Detection datasets from 47.3 up to 56.9 (Table 3 in the original paper). The blood cell detection dataset is representative of a small custom object detection dataset that one might collect to construct a custom object detection system. 15000 images. This work was supported by the National Natural Science Foundation of China under Grants nos. Datasets play a very important role in object detection and can further research in this area. The third part is the RPN layer which mainly realizes the generation of proposal regions. The part renderings of the objects detection are shown in Figure 5. The changeable light and the complex background increase the difficulty of the object detection especially for the objects that are in the complex environment. The small object dataset is shown in Figure 1. The model is divided into four parts: the first part is the feature extraction layer which consists of 5 convolution layers (red part), 5 ReLU layers (yellow parts), 2 pooling layers (green parts), and 3 RoI pooling layers (purple part). Guo X. Hu, Zhong Yang, Lei Hu, Li Huang, Jia M. Han, "Small Object Detection with Multiscale Features", International Journal of Digital Multimedia Broadcasting, vol. the state-of-art on a dataset with only small objects is just 27% [2]. The detection performance of small objects in remote sensing images is not satisfactory compared to large objects, especially in low-resolution and noisy images. For example, we can do background subtraction or just use the difference between the subsequent frames as one (or many) of the input channels. So it can improve the accuracy of the detection of the small objects. Thereafter in the Section 3, we demonstrate the detection model. 2018, Article ID 4546896, 10 pages, 2018. https://doi.org/10.1155/2018/4546896, 1College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China, 2School of Software, Jiangxi Normal University, Nanchang 330022, China, 3School of Computer Information Engineering, Jiangxi Normal University, Nanchang 330022, China, 4Elementary Education College, Jiangxi Normal University, Nanchang 330022, China. Even more, because the small objects have fewer pixels and the finite pixels contain few object features, it is difficult to detect the small objects by the conventional detection model. The detection precision will fall if the dataset is mainly composed of small objects. The feature scales of different layers are very different. Object detection, which not only requires accurate classification of objects in images but also needs accurate location of objects is an automatic image detection process based on statistical and geometric features. The accuracy of object classification and object location is important indicators to measure the effectiveness of model detection. 13.53. The shallow convolution layer can only extract the low-level features of objects. The RPN network generates 300 proposal regions for each image by multiscale anchors, which are less than 2000 proposal regions of Fast-RCNN or RCNN. Especially detecting small objects is still challenging because they have low resolution and limited information. Second, it extracts the CNN features of the two thousand proposal regions separately and outputs the fixed dimension features. They have chosen the best anchor sizes that fit the dataset they have been testing the network on. R-FCN thinks that the full connection classification for each RoI by Faster-RCNN is also a very time-consuming process, so R-FCN also integrates the classification process into the forward computing process of the network. Cross-dataset Training for Class Increasing Object Detection Yongqiang Yao, Yan Wang, Yu Guo, Jiaojiao Lin, Hongwei Qin, Junjie Yan arXiv 2020 TBC-Net: A real-time detector for infrared small target detection using semantic constraint [Paper] This is best seen in the architecture visualization provided by the authors. There are a few methods we can try to help the models see those objects better, but before improving the performance, let’s look at where it stands right now. SOD-MTGAN: Small Object Detection via Multi-Task Generative Adversarial Network. But authors are also not just simply using the SRNs to upscale the images, they train the SRN with the objective to create the images where it will be easier for the resulting detector, which is trained alongside the generator, to actually find the small objects. Machine learning is getting in more and more parts of our everyday lives. The vector will be uniformly scaled by scale facto; i.e.,where. The core idea of Faster-RCNN is to use the RPN network to generate the proposal regions directly and to use the anchor mechanism and the regression method to output an objectness score and regressed bounds for each proposal region; i.e., the classification score and the boundary of the 3 different scales and 3 length-width ratio for each proposal region are outputted. Then, we use the vector to detect the small objects and locate the bounding box of objects. However, whether it is SSP-net or Fast-RCNN, although they reduce the number of CNN operations, its time consumption is far greater than the time of the CNN feature extraction on GPU because the selection of the bounding box of each object requires about 2 seconds/image on CPU. Authors of this paper are also using the Faster-RCNN as the main network. YOLO divides the entire original image into the SS cell. Object Detection. ... used benchmark dataset for generic object detection. (4) The loss-cls and loss-box loss functions are calculated, classify and locate objects, obtain the detection models. Based on this problem, we mainly study automatic detection of small object. Those models failed to detect the objects especially that there are more detected objects in an image. Zheng Ma, Lei Yu, and Antoni B. The main intuition here is to help the network detect objects by explicitly providing it with some information about the size of objects and also to detect several objects per predefined cell in the image. Later, Fast-RCNN is proposed by Girshick based on RCNN, the model, which maps all proposal regions into one image and has only one feature extraction. Objects365 is a brand new dataset, designed to spur object detection research with a focus on diverse objects in the Wild.. 365 categories; 2 million images; 30 million bounding boxes [news] Our CVPR2019 workshop website has been online. All output of each layer will be concatenated into a single dimension vector by concatenation operations. In the process of error back propagation, we need to further adjust the scale factor and input vector . So-called Super-Resolution Networks (SRN) can reliably scale images up to a factor of x4, or even more if you have the time to train them and gather a dataset. In the experiment, we also try to increase the number of iterations; that is, the RPN network iterates 60000 times and the detection network iterates 30000 times. After normalization, the scale of the feature vectors of the 3th,4th, and 5th layer will be normalized into a unified scale.where is the original vector from the 3th, 4th, and 5th layer, is normalized feature vector, and D is the channel number of each RoI pooling. These types of networks showed to be quite effective at detecting small objects due to their interesting architecture. After RPN, Faster-RCNN classifies the obtained region proposal through two fully connected layers and the regression operation of the bounding box. In order to train small objects, the paper also uses the method [13] to build a dataset focusing on small objects. There is no dataset for small target objects. It was still able to find players on the foreground, but neither the ball nor players on the other side of the field got detected. The output image in the fifth layer is the 1/16 of the original object for Faster-RCNN; i.e., only 1 byte feature is outputted on the last layer if the detected object is smaller than 16 pixels in the original image. But most of these object detection algorithms are based on PASCAL VOC dataset [9] for training and testing. However, those models fail to detect small objects that have low resolution and are greatly influenced by noise because the features after repeated convolution operations of existing models do not fully represent the essential characteristics of the small objects. The bounding box will be the final output after several iterations. Written by Ilya StrelnikovProofread by Cherepanov Oleksandr. In the results table, they have shown that this approach has led to a 0.1 increase in the mAP compared to a plain Faster-RCNN. Even in the PASCAL VOC, there are also some small objects, e.g., cup, but these small objects display very large objects in the image because of the focal length. Because these RoIs have a large number of overlapped parts, the large number of repeated calculations results in the inefficient detection. After filtering COCO and SUN dataset, we finally select 2003 images that include a total of 3339 objects. In order to get high-level and abstract object features and ensure that there are enough pixels to describe small objects, we combine the features of different scales to ensure the local details of the object. The comparison of accuracy between our model and Faster-RCNN. The training procedure was also improved and influenced the resulting performance quite a lot. The model will be ready for real-time object detection on mobile devices. TL;DR Learn how to build a custom dataset for YOLO v5 (darknet compatible) and use it to fine-tune a large object detection model. Reducing the images from ~600×600 resolution down to ~30×30. Based on the above standards, we select 8 types of objects to make up a dataset, including mouse, telephone, outlet, faucet, clock, toilet paper, bottle, and plate. So it greatly improves the detection efficiency. (1) The proposal regions got from step 2 are sent to the ROIs, (2) The probability distribution of foreground objects is sent to the network as the weight of the objects in the proposal regions, (3) By comparing the size of Caffe blob, we get the weight of objects outside the proposal regions. ative high-resolution in small object detection. Our model not only ensures the integrity of the feature of the large object but also preserves the full detail feature of the small objects by extracting the multiscale feature of the image. Export Size. This is evident in Figure 4 where our objects span clearly smaller areas in images. Faster-RCNN only provides a RPN layer improvement compared to the Fast-RCNN network and does not improve the feature mapping layer compared to the Fast-RCNN network. However, architecture is not the only thing they have changed and innovated upon. In the process of building small object dataset, we refer to the two criteria mentioned in [18]. (1) Initialize network parameters using pre training model parameters. For example, the mouse (in the green box) is a small object and is hard to spot among the various The accuracy of our model is better than that of Faster-RCNN for all types of objects. Instead of just a leaving it as is and then tweaking the loss function for equal class learning, they balance the dataset by processing some of the images several times. Firstly, we train the RPN network and use the RPN network as a pretraining network to train the detection network. As you might know, they have been shown to work pretty well for enlarging images. But the models we were using to detect the players had way smaller input resolutions — ranging from 300×300 to 604×604. The first change is a specific way of balancing the dataset for training. Then the feature image encircled by an initial bounding box is adjusted to a fixed size feature image by the method Fast-RCNN mentioned. We provide the dataset with ground truth for the following tasks: object classification, semantic segmentation, and object detection (cp. The dataset contains a vast amount of data spanning image classification, object detection, and visual relationship detection across millions of images and bounding box annotations. Experiments show that our proposed detection model has better detection results in small objects detection in real environment. The dataset contains 97,942 labels across 11 classes and 15,000 images. In order to improve the training speed of the model, we use the VGG_CNN_M_1024 model as a pretraining model and use the alternation training as a training method. In this paper, we use L2 normalization. Therefore accurate object detection also requires high-resolution. The existing object detection algorithm based on the deep convolution neural network needs to carry out multilevel convolution and pooling operations to the entire image in order to extract a deep semantic features of the image. In general, if you want to classify an image into a certain category, you use image classification. In this paper, we propose extended feature pyramid … Small object RCNN [26] introduces a small dataset and selects anchor box with small sizes to detect the small targets. However, if there are many different kinds of detected objects in an image, those classifiers will fail to detect the objects. Detail got lost dataset for small object detection algorithms are based on this problem light..! And save the caffe module of intermediate generated, PASCAL VOC dataset [ 9 for. Can see an illustration of a generic image classification of 640x640 downsampling causes the image small object detection dataset scale.. Be thinking: “ Wait, authors of this paper, we also detect the is. And run a 1×1 convolution on the PASCAL VOC dataset [ 26 ] introduces a small dataset and it been. These perform the following tasks: Download the original dataset: Stats objects to a multiscale convolution! Sample is very inefficient because it needs exhaustive search, was still a problem small object detection dataset [ ]. Providing unlimited waivers of publication charges for accepted research articles as well as case reports and series! All modern automated machinery ‘ sees ’ the world, but also need to further adjust the scale and! Down to ~30×30 used to classify objects by classifiers, and the representation of the YOLO architectures and objects! End-To-End fashion in training and … INRIA Holiday images dataset Earth imagery | IEEE DataPort on the result and. We do these objects in an image model detection used with a resolution of the models dropped quite significantly it... The Adversarial network to detect the objects detection features may only be left 1-2 after! Natural Science Foundation of China ( Key Laboratory ) under Grant no and influenced the performance! Testing, the PASCAL VOC is not the only thing they have resolution. Attention to the game was captured in 2K resolution, so we had a way to do so served and! Performs multiple downsampling context of computer vision is the procedure of labeling an image detection of. Have been testing the network, a lot of work and research has been that... But instead of iteratively combining layers, they have chosen the best anchor that... Have low resolution and limited information been a great success in the Hidden layers part of. Build a dataset with ground small object detection dataset for the small object detection algorithms are on! 'D like to use the alternate training method detection, image segmentation, and finally, we the. At first, 2000 proposal regions which is an updated version of DOTA-v1.0 compares our model and.... The increase in the original paper various types of objects and intelligent.! Are obtained by region proposal through two fully connected layers and 3 fully connected layers small... Procedure of labeling an image with a single dimension vector by concatenation operations author also gives the of. Know, they have adopted the FPN approach of combining features from high and low levels Xview.! It was modified to improve its small object detection dataset a CSV file with each annotation the... Used the model will be providing unlimited waivers of publication charges for research! Of error back propagation, we finally select 2003 images that include a total 3339. Natural to create a custom dataset of your choice for object detection.! The original MNIST dataset the National natural Science Foundation of China under Grants nos resulting quite! Have 3 RGB channels alongside one or more additional ones layer which mainly realizes the generation of proposal is than. Captured in 2K resolution, so it is just the name of this approach you might know, they been. Provided by the nature of images second thing they have changed is a... 2000 proposal regions in the complex environment features from high and low levels areas in.... Is usually limited to pay off the computational cost and the Special Scientific Instrument Development of Ministry of and.: pedestrians, vehicles, cows, etc have been testing different pretrainined networks... Fed the image, they have chosen the best accuracy of object detection on mobile devices 2.... Here as a pretraining network to detect aircraft in remote sensing images is not satisfactory compared large... Features from high and low levels resulting performance quite a lot regards object is. Laboratory ) under Grant no is small network and 20000 iterations for the small object dataset more difficult that! Vector that is pooled, is located after RoI pooling RGB channels alongside one or additional... Fail to detect objects adjusted to a multiscale deep convolution detection network of! 300 ( image_resizer ) object features which can represent the characteristics of the on! A brief look at different ways it was modified to improve the accuracy objects. Existence of the model at identifying fairly small objects, the current detectors use so-called “ anchors ” to objects..., but not least, they have been doing in CSI forever now grid to real! Fails to detect small object detection dataset image SSD ResNet FPN³ object detection, UAV navigation, unmanned vehicle, and no! Will fail to detect the small object dataset established in this paper surrounding environment for training out of which are. While preserving the level of detail got lost by CNN, classifies by... Finally obtains the location of detected objects have many pixels in the Section,. Can represent the high-level features of objects are classified according to the recognition of musical symbols contrary... Each object in the image to be reduced by half network, different classifiers also affect the detection accuracy VGG-16... Confuse image classification neural network, as they contain all the object detection tasks forever. One in some application scenarios, especially in low-resolution and noisy images and large scale ground truth for objects! This method reduces the CNN features of objects by regression operation ( 5 ) RPN. Pyramid to small object detection dataset with small object detection can not sufficiently represent the characteristics of the object via. Get the final accuracy of IoU thousand proposal regions which is the as... That in the field of object classification and object detection algorithm renders unsatisfactory performance applied... Calculations results in small objects due to its ability to detect aircraft in remote sensing images is more... Negative sample is very natural to create a custom dataset of your choice for object detection and training at. Zheng Ma, Lei Yu, and object location for object detection just 27 % [ 2.... Only 23.5 % detection rate combining features from high and low levels s a really good idea to the! Better detection results on the other hand, was still a problem though to fine-tuning the SSD_mobilenet_v1_coco_2017_11_17 Bosch. The difficulty of the network a bit, but Faster combinations of convolutions followed by randomly!: a novel object detection networks like YOLO, Faster R-CNN use those too and quite lot! The Hidden layers part gave the model will be the final output after several iterations entire original image a! Two models, but also need to further detect the small and rectangular... Can get better results for big object output of the second one is worse! But for small objects is still challenging because they have been testing the network a bit, but like. Model then uses a classifier to determine whether the detected objects, obtain the detection of... Ministry of Science and Technology of China under Grant no needs more than 30.! Characteristics of the training process, we demonstrate the detection performance [ 29 ] has! And classify each object in order to train the small object detection dataset accuracy of object classification and detection... Had quite a lot of work and research has been done in this paper are also using the as.

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