Improved X-ray Prohibited Items Detection Method Based on Lightweight Convolution Blocks and SCAM

doi:10.3778/j.issn.1673-9418.2404059

Abstract

Abstract: To resolve the problems of high overlap, occlusion, difficulty in extracting key feature information, and complex background interference in X-ray contraband images, an improved X-ray contraband detection model based on multi-branch lightweight convolution and attention mechanism is proposed. The proposed model designs the backbone network with the spatial-channel attention mechanism (SCAM), which distinguishes redundant and non-redundant information in feature maps by reorganizing them in the channel and spatial dimensions, enhancing the extraction of key features and suppressing background interference, and also improving the ability of the model to detect contraband in complex scenes. A multi-branch lightweight convolution structure (MLCB) is proposed, which uses a lightweight dual-branch and information compensation branch to process feature maps to reduce the number of model parameters, so as to improve the operation efficiency. In addition, the minimum point distance intersection over union (MPDIoU) loss function and soft non-maximum suppression (Soft NMS) are integrated to replace the complete intersection over union (CIoU) loss function. By defining a more comprehensive intersection over union method, it alleviates the difficulty in optimization when bounding boxes overlap, and improves the problems of false positive and false negative caused by overlap of contraband. The proposed model is verified on OPIXray, HIXray and SIXray datasets, and mAP0.50 reaches 95.7%, 83.7% and 95.3%, respectively. The results show that the proposed method has high accuracy and strong robustness with a small amount of calculation, and it can solve the problems of overlap and occlusion as well as missed and false detections effectively.

Key words: X-ray images, contraband detection, spatial and channel reconstruction, multi-branch light convolution, loss function

摘要： 针对X光违禁品图像目标重叠遮挡、关键特征信息提取困难和复杂背景干扰等问题，提出了多分支轻量化卷积和注意力机制改进的X光违禁品检测模型。所提模型在主干网络设计空间和通道重构注意力机制（SCAM），通过对特征图在通道和空间上重组，区分特征图冗余信息和非冗余信息，加强关键特征提取并抑制背景干扰，提升模型面对复杂场景检测违禁品的能力；提出多分支轻量化卷积结构（MLCB），采用轻量化双分支和信息补偿分支共同处理特征图，降低模型参数量并防止模型预测精度下滑，提升运行效率；同时融合最小交并比（MPDIoU）损失函数和软非极大值抑制（Soft NMS）替换完全交并比（CIoU）边框回归损失函数，通过定义更加全面的交并比方式，缓解边框回归重合情况下难以优化问题，改善违禁品重叠遮挡造成的易漏检误检问题。所提模型在OPIXray、HIXray与 SIXray三个数据集上进行验证，mAP0.50分别达到了95.7%、83.7%和95.3%。实验结果表明所提方法在计算量较小的情况下，具备高精准度和强鲁棒性，可以有效解决重叠遮挡和漏报、误报等问题。

关键词: X光图像, 违禁品检测, 空间和通道重构, 多分支轻量化卷积, 损失函数

ZUO Jing, SHI Yangyu, LU Shuhua. Improved X-ray Prohibited Items Detection Method Based on Lightweight Convolution Blocks and SCAM[J]. Journal of Frontiers of Computer Science and Technology, 2025, 19(6): 1598-1610.

左景, 石洋宇, 卢树华. 基于轻量化卷积和SCAM改进的X光违禁品检测[J]. 计算机科学与探索, 2025, 19(6): 1598-1610.

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