基于改进Cascade RCNN网络的X光安检违禁品检测

针对X光安检违禁品检出率低下的问题, 提出了一种基于改进Cascade RCNN网络的X光安检违禁品检测算法. 该算法在网络结构上引入批特征擦除(batch feature erasing, BFE)模块. BFE模块通过随机擦除相同区域来增强局部特征学习, 进而强化网络对剩余特征的学习表达. 此外, 针对检出率低下问题, 在该算法中提出加权SD loss损失函数, 该损失函数使用权重融合的方式将Smooth L1 loss与DIoU loss进行加权融合, 通过改变权重比例系数, 能够使目标检测结果更加准确, 一定程度上提高了检出率. 实验结果表明: 在公开的X光安检违禁品数据集上, 测试性能与原算法相比, 改进Cascade RCNN网络对X光安检违禁品检出率增长了3.11%, 改进算法的识别精度有一定的提高.

X-ray Security Inspection for Contraband Detection Based on Improved Cascade RCNN Network

Considering the low detection rate of X-ray security inspection of contraband, an algorithm based on the improved Cascade RCNN is proposed. By this algorithm, a batch feature erasing (BFE) module is introduced into the network structure, which can enhance local feature learning by randomly erasing the same region and thus further enhance the learning expression of residual features. In addition, the weighted SD loss function is presented in this algorithm to solve the problem of low detection rates, which employs weight fusion to fuse Smooth L1 loss and DIoU loss, and by changing the proportion coefficient of weights, it can make the detection result more accurate. The experimental results show that the detection rate of the improved Cascade RCNN on an open contraband dataset for X-ray security inspection is increased by 3.11% compared with that of the original algorithm, and the accuracy of the improved algorithm is raised.