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YOLOv4-dense: A smaller and faster YOLOv4 for real-time edge-device based object detection in traffic scene

文献类型：期刊论文

作者Jiang, Yue1,2,3; Li, Wenjing2,3; Zhang, Jun2,3; Li, Fang2,3; Wu, Zhongcheng2,3
刊名IET IMAGE PROCESSING
出版日期2022-11-08
ISSN号1751-9659
DOI10.1049/ipr2.12656
通讯作者Zhang, Jun(zhang_jun@hmfl.ac.cn)
英文摘要Edge-device-based object detection is crucial in many real-world applications, such as self-driving cars, ADAS, driver behavior analysis. Although deep learning (DL) has become the de-facto approach for object detection, the limited computing resources of embedded devices and the large model size of current DL-based methods increase the difficulty of real-time object detection on edge devices. To overcome these difficulties, in this work a novel YOLOv4-dense model is proposed to detect objects in an accurate, fast manner, which is built on top of the YOLOv4 framework but with substantial improvements. More specifically, lots of CSP layers are pruned since it will decrease inference speed. And to address the losing small objects problem, a dense block is introduced. In addition, a lightweight two-stream YOLO head is also designed to further reduce the computational complexity of the model. Experimental results on NVIDIA JETSON TX2 embedded platform demonstrate that YOLOv4-dense can achieve a higher accuracy, faster speed with smaller model size. For instance, on the KITTI dataset, YOLOv4-dense obtains 84.3% mAP and 22.6 FPS with only 20.3 M parameters, surpassing the state-of-the-art models with comparable parameter budget such as YOLOv3-tiny, YOLOv4-tiny, PP-YOLO-tiny by a large margin.
资助项目Research on Scientific Data Management Method and Key Technology of Large-scale Scientific Facility ; Pre-research Project on Key Technologies of Integrated Experimental Facilities of Steady High Magnetic Field and Optical Spectroscopy ; High Magnetic Field Laboratory of Anhui Province ; High Magnetic Field Laboratory, Chinese Academy of Sciences[2019HSC-KPRD003]
WOS研究方向Computer Science ; Engineering ; Imaging Science & Photographic Technology
语种英语
出版者WILEY
WOS记录号WOS:000879795300001
资助机构Research on Scientific Data Management Method and Key Technology of Large-scale Scientific Facility ; Pre-research Project on Key Technologies of Integrated Experimental Facilities of Steady High Magnetic Field and Optical Spectroscopy ; High Magnetic Field Laboratory of Anhui Province ; High Magnetic Field Laboratory, Chinese Academy of Sciences
源URL[http://ir.hfcas.ac.cn:8080/handle/334002/130130]  
专题中国科学院合肥物质科学研究院
通讯作者Zhang, Jun
作者单位1.High Magnet Field Lab Anhui Prov, Hefei, Peoples R China
2.Chinese Acad Sci, High Magnet Field Lab, HFIPS, Hefei 230031, Peoples R China
3.Univ Sci & Technol China, Hefei, Peoples R China
推荐引用方式
GB/T 7714		 Jiang, Yue,Li, Wenjing,Zhang, Jun,et al. YOLOv4-dense: A smaller and faster YOLOv4 for real-time edge-device based object detection in traffic scene[J]. IET IMAGE PROCESSING,2022.
APA Jiang, Yue,Li, Wenjing,Zhang, Jun,Li, Fang,&Wu, Zhongcheng.(2022).YOLOv4-dense: A smaller and faster YOLOv4 for real-time edge-device based object detection in traffic scene.IET IMAGE PROCESSING.
MLA Jiang, Yue,et al."YOLOv4-dense: A smaller and faster YOLOv4 for real-time edge-device based object detection in traffic scene".IET IMAGE PROCESSING (2022).

入库方式： OAI收割

来源：合肥物质科学研究院

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