MC-YOLOv5: A Multi-Class Small Object Detection Algorithm
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Published:2023-08-02
Issue:4
Volume:8
Page:342
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ISSN:2313-7673
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Container-title:Biomimetics
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language:en
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Short-container-title:Biomimetics
Author:
Chen Haonan1, Liu Haiying1, Sun Tao1, Lou Haitong1, Duan Xuehu1, Bi Lingyun1, Liu Lida2
Affiliation:
1. School of Information and Automation Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, China 2. Shandong Runyi Intelligent Technology Co., Ltd., Jinan 250002, China
Abstract
The detection of multi-class small objects poses a significant challenge in the field of computer vision. While the original YOLOv5 algorithm is more suited for detecting full-scale objects, it may not perform optimally for this specific task. To address this issue, we proposed MC-YOLOv5, an algorithm specifically designed for multi-class small object detection. Our approach incorporates three key innovations: (1) the application of an improved CB module during feature extraction to capture edge information that may be less apparent in small objects, thereby enhancing detection precision; (2) the introduction of a new shallow network optimization strategy (SNO) to expand the receptive field of convolutional layers and reduce missed detections in dense small object scenarios; and (3) the utilization of an anchor frame-based decoupled head to expedite training and improve overall efficiency. Extensive evaluations on VisDrone2019, Tinyperson, and RSOD datasets demonstrate the feasibility of MC-YOLOv5 in detecting multi-class small objects. Taking VisDrone2019 dataset as an example, our algorithm outperforms the original YOLOv5L with improvements observed across various metrics: mAP50 increased by 8.2%, mAP50-95 improved by 5.3%, F1 score increased by 7%, inference time accelerated by 1.8 ms, and computational requirements reduced by 35.3%. Similar performance gains were also achieved on other datasets. Overall, our findings validate MC-YOLOv5 as a viable solution for accurate multi-class small object detection.
Funder
Haiying Liu Innovation Ability Enhancement Project of Shandong Province Science and Technology Small and Medium Enterprises, Research and Application of Key Technologies for Data Driven Unmanned Security System
Subject
Molecular Medicine,Biomedical Engineering,Biochemistry,Biomaterials,Bioengineering,Biotechnology
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