自适应与多尺度特征融合的X光违禁品检测OACSTPCD

To resolve the problems of spatial multi-scale variation,background interference and model complex of X-ray security inspection contraband images,a lightweight YOLOv5 model with spatial adaptation and multi-scale feature fusion is proposed.Taking YOLOv5 as the basic framework,the adaptive spatial feature fusion mechanism is introduced to suppress the influence of feature scale differences,and the bidirectional feature weighted fusion is integrated with the bidirectional feature pyramid network;the lightweight channel attention mechanism is used to obtain accurate position infor-mation and enhance the expression of effective features.Meanwhile,GhostConv is used to replace part of Conv to reduce the computational complexity of the network.This model achieves mAP of 94.2%,92.8%and 83.3%on three public data-sets such as OPIXray,SIXray and HiXray,respectively,which is 5.4,0.5 and 1.7 percentage points higher than the base-line model.And the model training time is not significantly increased.It takes into account the accuracy and speed of model detection,which is superior to many current advanced algorithms.