基于改进YOLOX的X射线违禁物品检测

在安全检查过程中快速准确地识别违禁物品有利于维护公共安全。针对X射线行李图像中存在的物品堆叠变形、复杂背景干扰、小尺寸违禁物品检测等问题，提出一种改进模型用于违禁物品检测。改进基于YOLOX模型进行，首先在主干网络中引入注意力机制加强神经网络对违禁品的感知能力；其次在Neck部分改进多尺度特征融合方式，在特征金字塔结构后加入Bottom-up结构，增强网络细节表现能力以此提高对小目标的识别率；最后针对损失函数计算的弊端改进IOU损失的计算方式，并根据违禁物品检测任务特点改进各类损失函数的权重，增大对网络误判的惩罚来优化模型。使用该改进模型在SIXray数据集上进行实验，m AP达到89.72%,FPS到达111.7 frame/s具备快速性和有效性，所提模型与阶段主流模型相比准确率和检测速度都有所提升。

X-ray Detection of Prohibited Items Based on Improved YOLOX

In the process of security inspection, rapid and accurate identification of prohibited items is conducive to maintaining public security. To address the problems of stack deformation, complex background interference, and small-sized contraband detection in X-ray luggage images, an improved model for contraband detection is proposed. This improvement is based on the YOLOX model. First, an attention mechanism was introduced into the backbone network to enhance the ability of the neural network to perceive contrabands. Second, in the neck part, the multi-scale feature fusion method was improved upon, and a bottom-up structure was added after the feature pyramid structure to enhance the performance ability of the network for details, thereby improving the recognition rate of small targets. Finally, the calculation method based on IOU loss was upgraded in view of the disadvantages of the loss function calculation. The weights of various loss functions were also increased according to the characteristics of the contraband detection task, and the punishment of network misjudgment was increased to optimize the model. Upon using the improved model on the SiXray dataset, an mAP of 89.72% was attained and a fast and effective FPS arrival rate of 111.7 frame/s was achieved. Compared with mainstream models, the accuracy and detection speed of the proposed model were improved.