A Multi-object Prohibited Items Identification Algorithm Based on Semantic Segmentation

The semantic segmentation algorithm based on deep learning can realize the automatic identification of prohibited items in security inspection and obtain the location, category and shape information of prohibited items. However, the traditional semantic segmentation algorithm performs poorly in recognizing prohibited items of different sizes and various objects. In response to this question, this paper proposed a multi-object prohibited items recognition algorithm based on semantic segmentation technology. In the encoding stage, the atrous spatial pyramid convolution block (ASPC) was designed to improve the ability of the network to mine multi-scale information of the feature map. At the same time, attention mechanism was introduced to supervise the feature extraction process of ASPC module and further improve the feature extraction ability of the module. In the decoding stage, inspired by U-Net model, we adopt step-by-step upsampling operation and added 1 × 1 convolution to reduce the channel dimension, the computation was reduced and the running speed of the model is improved. Experimental results show that the proposed algorithm performs well in the multi-object prohibited items recognition task. The score of mean intersection over union is 78.62, and the processing time of a single image is 68 ms.