Book ChapterDOI
VisDrone-DET2018: The Vision Meets Drone Object Detection in Image Challenge Results
Pengfei Zhu,Longyin Wen,Dawei Du,Xiao Bian,Haibin Ling,Qinghua Hu,Qinqin Nie,Hao Cheng,Chenfeng Liu,Xiaoyu Liu,Wenya Ma,Haotian Wu,Lianjie Wang,Arne Schumann,Chase Brown,Chen Qian,Chengzheng Li,Dongdong Li,Emmanouil Michail,Fan Zhang,Feng Ni,Feng Zhu,Guanghui Wang,Haipeng Zhang,Han Deng,Hao Liu,Haoran Wang,Heqian Qiu,Honggang Qi,Honghui Shi,Hongliang Li,Hongyu Xu,Hu Lin,Ioannis Kompatsiaris,Jian Cheng,Jianqiang Wang,Jianxiu Yang,Jingkai Zhou,Juanping Zhao,K J Joseph,Kaiwen Duan,Karthik Suresh,Bo Ke,Ke Wang,Konstantinos Avgerinakis,Lars Sommer,Lei Zhang,Li Yang,Lin Cheng,Lin Ma,Liyu Lu,Lu Ding,Minyu Huang,Naveen Kumar Vedurupaka,Nehal Mamgain,Nitin Bansal,Oliver Acatay,Panagiotis Giannakeris,Qian Wang,Qijie Zhao,Qingming Huang,Qiong Liu,Qishang Cheng,Qiuchen Sun,Robert Laganiere,Sheng Jiang,Shengjin Wang,Shubo Wei,Siwei Wang,Stefanos Vrochidis,Sujuan Wang,Tiaojio Lee,Usman Sajid,Vineeth N Balasubramanian,Wei Li,Wei Zhang,Weikun Wu,Wenchi Ma,Wenrui He,Wenzhe Yang,Xiaoyu Chen,Xin Sun,Xinbin Luo,Xintao Lian,Xiufang Li,Yangliu Kuai,Yali Li,Yi Luo,Yifan Zhang,Yiling Liu,Ying Li,Yong Wang,Yongtao Wang,Yuanwei Wu,Yue Fan,Yunchao Wei,Yuqin Zhang,Zexin Wang,Zhangyang Wang,Zhaoyue Xia,Zhen Cui,Zhenwei He,Zhipeng Deng,Zhiyao Guo,Zichen Song +104 more
- pp 437-468
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TLDR
A large-scale drone-based dataset, including 8, 599 images with rich annotations, including object bounding boxes, object categories, occlusion, truncation ratios, etc, is released, to narrow the gap between current object detection performance and the real-world requirements.Abstract:
Object detection is a hot topic with various applications in computer vision, e.g., image understanding, autonomous driving, and video surveillance. Much of the progresses have been driven by the availability of object detection benchmark datasets, including PASCAL VOC, ImageNet, and MS COCO. However, object detection on the drone platform is still a challenging task, due to various factors such as view point change, occlusion, and scales. To narrow the gap between current object detection performance and the real-world requirements, we organized the Vision Meets Drone (VisDrone2018) Object Detection in Image challenge in conjunction with the 15th European Conference on Computer Vision (ECCV 2018). Specifically, we release a large-scale drone-based dataset, including 8, 599 images (6, 471 for training, 548 for validation, and 1, 580 for testing) with rich annotations, including object bounding boxes, object categories, occlusion, truncation ratios, etc. Featuring a diverse real-world scenarios, the dataset was collected using various drone models, in different scenarios (across 14 different cities spanned over thousands of kilometres), and under various weather and lighting conditions. We mainly focus on ten object categories in object detection, i.e., pedestrian, person, car, van, bus, truck, motor, bicycle, awning-tricycle, and tricycle. Some rarely occurring special vehicles (e.g., machineshop truck, forklift truck, and tanker) are ignored in evaluation. The dataset is extremely challenging due to various factors, including large scale and pose variations, occlusion, and clutter background. We present the evaluation protocol of the VisDrone-DET2018 challenge and the comparison results of 38 detectors on the released dataset, which are publicly available on the challenge website: http://www.aiskyeye.com/. We expect the challenge to largely boost the research and development in object detection in images on drone platforms.read more
Citations
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Proceedings ArticleDOI
Clustered Object Detection in Aerial Images
TL;DR: Zhang et al. as discussed by the authors proposed a cluster proposal sub-network (CPNet), a scale estimation sub-networks (ScaleNet), and a dedicated detection network (DetecNet) to detect small objects in aerial images.
Posted Content
Vision Meets Drones: Past, Present and Future
TL;DR: The VisDrone dataset, which is captured over various urban/suburban areas of 14 different cities across China from North to South, is described, being the largest such dataset ever published, and enables extensive evaluation and investigation of visual analysis algorithms on the drone platform.
Proceedings ArticleDOI
Density Map Guided Object Detection in Aerial Images
TL;DR: This paper proposes a Density-Map guided object detection Network (DMNet), which is inspired from the observation that the object density map of an image presents how objects distribute in terms of the pixel intensity of the map.
Proceedings ArticleDOI
SlimYOLOv3: Narrower, Faster and Better for Real-Time UAV Applications
TL;DR: SlimYOLOv3 as mentioned in this paper proposes to learn efficient deep object detectors through channel pruning of convolutional layers, which enforce channel-level sparsity of CNNs by imposing L1 regularization on channel scaling factors and prune less informative feature channels to obtain "slim" object detectors.
Journal ArticleDOI
Advancing Image Understanding in Poor Visibility Environments: A Collective Benchmark Study
Wenhan Yang,Ye Yuan,Wenqi Ren,Jiaying Liu,Walter J. Scheirer,Zhangyang Wang,Taiheng Zhang,Qiaoyong Zhong,Di Xie,Shiliang Pu,Yuqiang Zheng,Yanyun Qu,Yuhong Xie,Liang Chen,Zhonghao Li,Chen Hong,Hao Jiang,Siyuan Yang,Yan Liu,Xiaochao Qu,Pengfei Wan,Shuai Zheng,Minhui Zhong,Taiyi Su,Lingzhi He,Yandong Guo,Yao Zhao,Zhenfeng Zhu,Jinxiu Liang,Jingwen Wang,Tianyi Chen,Yuhui Quan,Yong Xu,Bo Liu,Xin Liu,Qi Sun,Tingyu Lin,Xiaochuan Li,Feng Lu,Lin Gu,Shengdi Zhou,Cong Cao,Shifeng Zhang,Cheng Chi,Chubing Zhuang,Zhen Lei,Stan Z. Li,Shizheng Wang,Ruizhe Liu,Dong Yi,Zheming Zuo,Jianning Chi,Huan Wang,Kai Wang,Yixiu Liu,Xingyu Gao,Zhenyu Chen,Chang Guo,Yongzhou Li,Huicai Zhong,Jing Huang,Heng Guo,Jianfei Yang,Wenjuan Liao,Jiangang Yang,Liguo Zhou,Mingyue Feng,Likun Qin +67 more
TL;DR: The UG2+ challenge Track 2 competition in IEEE CVPR 2019 is launched, aiming to evoke a comprehensive discussion and exploration about whether and how low-level vision techniques can benefit the high-level automatic visual recognition in various scenarios.
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Book ChapterDOI
Microsoft COCO: Common Objects in Context
Tsung-Yi Lin,Michael Maire,Serge Belongie,James Hays,Pietro Perona,Deva Ramanan,Piotr Dollár,C. Lawrence Zitnick +7 more
TL;DR: A new dataset with the goal of advancing the state-of-the-art in object recognition by placing the question of object recognition in the context of the broader question of scene understanding by gathering images of complex everyday scenes containing common objects in their natural context.