Volume 14 Issue 6
Nov.  2021
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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

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

doi: 10.37188/CO.2021-0078
Funds:  Supported by National Natural Science Foundation of China (No. 61671190, No. 61801149); Japan Society for the Promotion of Science (No. #20K11873)
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  • Corresponding author: aili925@hrbust.edu.cn
  • Received Date: 2021-04-13
  • Rev Recd Date: 2021-05-11
  • Available Online: 2021-08-11
  • Publish Date: 2021-11-19
  • 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.
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