基于反向瓶颈和LCBAM设计的X光违禁品检测

doi:10.3778/j.issn.1673-9418.2301041

摘要/Abstract

摘要： 针对X光违禁品图像姿态与角度变化易漏检误检及困难样本检测准确率低等问题，以YOLOv5网络为基线模型，提出一种融合了反向瓶颈结构和轻量化卷积块注意力模块设计的违禁品检测模型。在主干网络采用反向瓶颈结构设计注重细节特征信息，改进网络应对检测目标大角度变化问题；采用轻量化卷积块注意力机制抑制复杂背景干扰，降低模型参数量；此外，采用高斯误差线性单元激活函数和改进的置信度损失函数增强模型的非线性表达能力，加大对置信度预测的惩罚力度，优化网络对困难样本的检测性能。所提模型在三个大型公开数据集OPIXray、SIXray、HiXray上进行训练和测试，mAP分别达到了91.9%、93.4%和82.2%。结果表明，所提模型能够有效解决基线模型应对X光违禁品角度变化问题，具有较高的检测准确性和稳健性。

关键词: X光图像, 违禁品检测, 反向瓶颈, 轻量化卷积块注意力模块（LCBAM）

Abstract: To resolve the problems of position and angle change causing miss and false detection, low accuracy of difficult samples in X-ray luggage images, using YOLOv5 as the baseline, this paper proposes a model by inverted bottleneck and light convolution block attention module for the X-ray prohibited items detection. The inverted bottle-neck design is introduced in the backbone to emphasize the detailed features and improve the model to cope with the large-angle change problem. The light convolution block attention module is used to suppress background interference and reduce model parameter. The Gaussian error linear unit activation function and improved loss function are used to enhance the nonlinear expression ability, increasing the punishment of predicted value to optimize the model??s detection ability for difficult samples. The proposed model is trained and tested on three large public datasets OPIXray, SIXray, and HiXray, resulting in the mAP of 91.9%, 93.4%, and 82.2%, respectively. The results show that the proposed method can effectively solve the problem of angel change in X-ray luggage, indicating its high accuracy and robustness.

Key words: X-ray imagery, prohibited items detection, inverted bottleneck, light convolution block attention module (LCBAM)

董乙杉, 郭靖圆, 李明泽, 孙嘉傲, 卢树华. 基于反向瓶颈和LCBAM设计的X光违禁品检测[J]. 计算机科学与探索, 2024, 18(5): 1259-1270.

DONG Yishan, GUO Jingyuan, LI Mingze, SUN Jia'ao, LU Shuhua. X-ray Prohibited Items Detection Based on Inverted Bottleneck and Light Convolution Block Attention Module[J]. Journal of Frontiers of Computer Science and Technology, 2024, 18(5): 1259-1270.

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