结合空洞卷积和迁移学习改进YOLOv4的X光安检危险品检测

吴海滨; 魏喜盈; 刘美红; 王爱丽; 刘赫; 岩堀祐之

doi:10.37188/CO.2021-0078

Volume 14 Issue 6

Nov. 2021

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Chinese Optics > 2021 > 14(6): 1417-1425.

WU Hai-bin, WEI Xi-ying, LIU Mei-hong, WANG Ai-li, LIU He, IWAHORI Yu-ji. Improved YOLOv4 for dangerous goods detection in X-ray inspection combined with atrous convolution and transfer learning[J]. Chinese Optics, 2021, 14(6): 1417-1425. doi: 10.37188/CO.2021-0078

Citation:

WU Hai-bin, WEI Xi-ying, LIU Mei-hong, WANG Ai-li, LIU He, IWAHORI Yu-ji. Improved YOLOv4 for dangerous goods detection in X-ray inspection combined with atrous convolution and transfer learning[J]. Chinese Optics, 2021, 14(6): 1417-1425. doi: 10.37188/CO.2021-0078

Citation:

WU Hai-bin, WEI Xi-ying, LIU Mei-hong, WANG Ai-li, LIU He, IWAHORI Yu-ji. Improved YOLOv4 for dangerous goods detection in X-ray inspection combined with atrous convolution and transfer learning[J]. Chinese Optics, 2021, 14(6): 1417-1425. doi: 10.37188/CO.2021-0078

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Improved YOLOv4 for dangerous goods detection in X-ray inspection combined with atrous convolution and transfer learning

doi: 10.37188/CO.2021-0078

WU Hai-bin^1
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WEI Xi-ying^1
,,
LIU Mei-hong^1
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WANG Ai-li^{1
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LIU He^1
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IWAHORI Yu-ji^2
,

1.
Heilongjiang Province Key Laboratory of Laser Spectroscopy Technology and Application, Harbin University of Science and Technology, Harbin 150080, China
2.
Department of Computer Science, Chubu University, Kasugai 487-8501, Japan

Funds: Supported by National Natural Science Foundation of China (No. 61671190, No. 61801149); Japan Society for the Promotion of Science (No. #20K11873)

More Information

Corresponding author: aili925@hrbust.edu.cn
Received Date: 13 Apr 2021
Rev Recd Date: 11 May 2021

Available Online: 11 Aug 2021

Publish Date: 19 Nov 2021

Abstract

Abstract

In response to the complex backgrounds of X-ray security images, serious overlap and occlusion phenomena, and the large differences in the placement and shape of dangerous goods, this paper improves the network structure of YOLOv4 for dangerous objects detection by combining atrous convolution with the Atrous Space Pyramid Pooling (ASPP) model to increase receptive field and aggregate multi-scale context information. Then, the K-means clustering method is used to generate an initial candidate frame that is more suitable for dangerous goods detection in X-ray inspection images. Cosine annealing is used to optimize the learning rate in model training to further accelerate model convergence and improve model detection accuracy. The experimental results show that the proposed ASPP-YOLOv4 in this paper can obtain an mAP of 85.23% on the SIXRay dataset. The model can effectively reduce the false detection rate of dangerous goods in X-ray security images and improve the detection ability of small targets.
- X-ray security images,
- YOLOv4,
- atrous convolution,
- spatial pyramid pooling,
- cosine annealing

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References(20)

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