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Research on Security Apparel Recognition Technology Based on Multiple Scene Perception

This paper proposes a SEBYOLO security apparel recognition model for the problem of construction workers not wearing security apparel in multiple scenarios. The model network weighs the different channels of the feature map and integrates the attention mechanism and the multi-layer feature network. Firstly, the model makes full use of the idea of features and strengthening small target detection to improve the feature extraction network. Secondly, an attention mechanism is introduced to improve the feature extraction ability and detection accuracy of the model. Secondly, the attention mechanism is introduced to improve the feature extraction ability and detection accuracy of the model. Taking into account the small targets of the head and the helmet, this paper further optimizes the feature scale of the feature fusion layer and the multi-scale detection layer, so that the detection model can better adapt to small targets. Finally, a comparative experiment was carried out on SEBYOLO and other detection models, and an ablation experiment was set up to comprehensively evaluate the model, verify the effectiveness of the model, and better reflect the performance of each network in the model. The experimental results show that the SEBYOLO model has improved accuracy, recall, and mAP compared to other detection models. In multiple scenarios, the accuracy of the model in detecting security apparel is 0.955, which can achieve accurate detection of security apparel targets.