Light-Head R-CNN: In Defense of Two-Stage Object Detector

Li, Zeming; Peng, Chao; Yu, Gang; Zhang, Xiangyu; Deng, Yansha; Sun, Jian

doi:10.48550/arxiv.1711.07264

Cited by 77 publications

(52 citation statements)

References 33 publications

(63 reference statements)

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“…Hence, a deep network is difficult to deploy on CPU devices (e.g., mobile phones). With the popularity of mobile devices, lightweight network with few parameters has received more and more attention from researchers, such as Light-Head R-CNN [74]. Thus, designing a lightweight network in weakly supervised object detection may be a new research direction.…”

Section: A Model Directionsmentioning

confidence: 99%

Deep Learning for Weakly-Supervised Object Detection and Object Localization: A Survey

Shao¹,

Chen²,

Shao³

et al. 2021

Preprint

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Weakly-Supervised Object Detection (WSOD) and Localization (WSOL), i.e., detecting multiple and single instances with bounding boxes in an image using image-level labels, are long-standing and challenging tasks in the CV community. With the success of deep neural networks in object detection, both WSOD and WSOL have received unprecedented attention. Hundreds of WSOD and WSOL methods and numerous techniques have been proposed in the deep learning era. To this end, in this paper, we consider WSOL is a sub-task of WSOD and provide a comprehensive survey of the recent achievements of WSOD. Specifically, we firstly describe the formulation and setting of the WSOD, including the background, challenges, basic framework. Meanwhile, we summarize and analyze all advanced techniques and training tricks for improving detection performance. Then, we introduce the widely-used datasets and evaluation metrics of WSOD. Lastly, we discuss the future directions of WSOD. We believe that these summaries can help pave a way for future research on WSOD and WSOL.

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Section: A Model Directionsmentioning

confidence: 99%

Deep Learning for Weakly-Supervised Object Detection and Object Localization: A Survey

Shao¹,

Chen²,

Shao³

et al. 2021

Preprint

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“…Table V shows the results of 10 baseline methods in the VisDrone-DET2019 Challenge, i.e., FPN [131], R-FCN [19], Faster R-CNN (FRCNN) [20], SSD [24], Cascade CNN [128], RetinaNet [130], CornetNet [211], RefineNet [212], DetNet [213], and Light Faster R-CNN (Light-RCNN) [214]. The samples are in strict accordance, with 6,471 for training, 548 for validation and 1,580 for testing.…”

Section: A Evaluation Of Object Detection From Uav-borne Imagesmentioning

confidence: 99%

Deep Learning for UAV-based Object Detection and Tracking: A Survey

Wu,

Li,

Hong

et al. 2021

Preprint

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This is the pre-acceptance version, to read the final version please go to IEEE Geoscience and Remote Sensing Magazine on IEEE Xplore. Owing to effective and flexible data acquisition, unmanned aerial vehicle (UAV) has recently become a hotspot across the fields of computer vision (CV) and remote sensing (RS). Inspired by recent success of deep learning (DL), many advanced object detection and tracking approaches have been widely applied to various UAV-related tasks, such as environmental monitoring, precision agriculture, traffic management. This paper provides a comprehensive survey on the research progress and prospects of DL-based UAV object detection and tracking methods. More specifically, we first outline the challenges, statistics of existing methods, and provide solutions from the perspectives of DL-based models in three research topics: object detection from the image, object detection from the video, and object tracking from the video. Open datasets related to UAV-dominated object detection and tracking are exhausted, and four benchmark datasets are employed for performance evaluation using some state-of-the-art methods. Finally, prospects and considerations for the future work are discussed and summarized. It is expected that this survey can facilitate those researchers who come from remote sensing field with an overview of DL-based UAV object detection and tracking methods, along with some thoughts on their further developments.

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“…Two object detector of this sort are CenterNet [45] and CornerNet-Lite [14], and they both perform very well in terms of efficiency and efficacy. For real-time object detection on CPU or mobile GPU, SSD-based Pelee [37], YOLOv3-based PRN [35], and Light-Head RCNN [17]-based ThunderNet [25] all receive excellent performance on object detection. [11].…”

Section: Related Workmentioning

confidence: 99%

CSPNet: A New Backbone that can Enhance Learning Capability of CNN

Wang¹,

Liao

Yeh³

et al. 2019

Preprint

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Neural networks have enabled state-of-the-art approaches to achieve incredible results on computer vision tasks such as object detection. However, such success greatly relies on costly computation resources, which hinders people with cheap devices from appreciating the advanced technology. In this paper, we propose Cross Stage Partial Network (CSPNet) to mitigate the problem that previous works require heavy inference computations from the network architecture perspective. We attribute the problem to the duplicate gradient information within network optimization. The proposed networks respect the variability of the gradients by integrating feature maps from the beginning and the end of a network stage, which, in our experiments, reduces computations by 20% with equivalent or even superior accuracy on the ImageNet dataset, and significantly outperforms state-of-the-art approaches in terms of AP 50 on the MS COCO object detection dataset. The CSPNet is easy to implement and general enough to cope with architectures based on ResNet, ResNeXt, and DenseNet. Source code is at https://github.com/WongKinYiu/CrossStagePartialNetworks.

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Light-Head R-CNN: In Defense of Two-Stage Object Detector

Cited by 77 publications

References 33 publications

Deep Learning for Weakly-Supervised Object Detection and Object Localization: A Survey

Deep Learning for Weakly-Supervised Object Detection and Object Localization: A Survey

Deep Learning for UAV-based Object Detection and Tracking: A Survey

CSPNet: A New Backbone that can Enhance Learning Capability of CNN

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