[1]WU Jun,DONG Jiaming,LIU Xin,et al.Lightweight object detection network and its application based on the attention optimization[J].CAAI Transactions on Intelligent Systems,2023,18(3):506-516.[doi:10.11992/tis.202206014]
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Lightweight object detection network and its application based on the attention optimization

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 18 Number of periods: 2023 3 Page number: 506-516 Column: 学术论文—机器感知与模式识别 Public date: 2023-07-05
Title:
Lightweight object detection network and its application based on the attention optimization
Author(s):
WU Jun1; 2;  DONG Jiaming1;  LIU Xin1;  WANG Chunzhi1
1. School of Computer Science, Hubei University of Technology, Wuhan 430068, China;
2. School of Materials Science and Engineering, Wuhan University of Technology, Wuhan 430070, China
Keywords:
object detection; deep learning; computer vision; lightweight network; coordinate attention; squeeze-and-excitation; one-stage object detection network; loss function
CLC:
TP18
DOI:
10.11992/tis.202206014
Abstract:
Taking the lightweight improved YOLO network as the main target, the new lightweight network models YOLOv5s-CCA (YOLOv5s-C3-coordinate attention) and YOLOv5s-CSE (YOLOv5s-C3-squeeze-and-excitation) are put forward in this paper by selecting the representative SE (squeeze-and-excitation) channel attention module and relatively novel CA (coordinate attention) spatial attention module to fuse with YOLOv5s object detection network. By further exploration, the strategy for the optimal insertion position of the SE and CA attention modules in YOLOv5s object detection network is demonstrated. The experiment proves that CA is superior to SE attention module in the lightweight network model. The YOLOv5s-CCA network model proposed in this paper realizes the goal of network lightweight in both PASCAL VOC 2012 and Global Wheat 2020 data sets, and its accuracy is improved compared with the original network. It is confirmed that YOLOv5s-CCA has certain universality and generalization, which provides reliable data support and certain reference value for its lightweight deployment in actual production and life.
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