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AC-YOLOv4: an object detection model incorporating attention mechanism and atrous convolution for contraband detection in x-ray images

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Abstract

The complex background of X-ray security detection images and the overlapping of contraband items with each other and their different sizes and locations lead to a high leakage rate and false detection of contraband items during the security screening process. To address the above problems, this paper proposes a target detection algorithm based on the YOLOv4 model with fused attention mechanism and atrous spatial pyramidal pooling, and calls it AC-YOLOv4. First, the original spatial pyramid pooling in YOLOv4 is replaced by atrous spatial pyramid pooling, which can enlarge the image receptive field and extract the features of contraband under different sizes. Second, the attention mechanism module is added to the neck part of the model to improve the extraction of deeper features of contraband and reduce background interference. Before training, we use K-means clustering algorithm to obtain the Anchor box which is more suitable for the specific X-ray security image dataset, and use transfer learning to train the network to accelerate the training speed of the model and improve the detection accuracy. The proposed X-ray security contraband detection model improves the recognition accuracy by 5.56%, 6.83% and 12.24% on the X-ray security datasets SIXray, OPIXray and XDXray respectively compared to the excellent SOTA target detection model – YOLOv7. The experimental results show that AC-YOLOv4 has a significantly improved detection capability compared to YOLOv4 and can effectively reduce the rate of missed and false detections of contraband in X-ray security screening, while improving the generalisation of the model for detecting contraband of different shapes and sizes.

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Data availability

The data that support the findings of this study are available from the corresponding author, upon reasonable request.

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Acknowledgements

this work was supported by the Xinjiang Autonomous Region Key R&D Project (2021B01002)

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  1. School of Software, Xinjiang University, Urumqi, 830000, China

    Bo Wang, Haoran Ding & Cheng Chen

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Wang, B., Ding, H. & Chen, C. AC-YOLOv4: an object detection model incorporating attention mechanism and atrous convolution for contraband detection in x-ray images. Multimed Tools Appl 83, 26485–26504 (2024). https://doi.org/10.1007/s11042-023-16628-8

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