Semantic segmentation and recognition of contraband for security X-ray images
In response to the technical challenges posed by the varying sizes,haphazard arrangement,and overlapping occlusion of prohibited items in security X-ray images,we propose an enhanced HRNet-based multi-scale feature fusion network model.This model aims to achieve automatic segmentation and recognition of prohibited items in images.In the encoding stage,we leverage the multi-resolution parallel network architecture of HRNet to extract multi-scale features,addressing the diverse scale of prohibited items in security X-ray images.In the decoding stage,a multi-level feature aggregation module is introduced that uses data-dependent upsampling instead of bilinear interpolation.upsampling to reduce information loss during aggregation,thus ensuring a more comprehensive representation of the features of the features extracted in the coding stage for a more complete characterisation of objects.In the overall architecture of the network,a de-obscuration module based on the attention mechanism is embedded to strengthen the edge-awareness ability of the model,alleviate the problem of serious overlapping occlusion of items in security X-ray images,and improve the segmentation and recognition accuracy of the model.By experimenting on the public dataset of PIDray security check images,the results show that the average intersection ratio of 73.15%,69.47%,and 58.33%are achieved in the three validation subsets of Easy,Hard,and Hidden,respectively,which are 0.49%,1.17%,and 5.69%,respectively,and the overall average intersection ratio is improved by about 2.45%.
NETL
NSTL
万方数据
