Abstract:
Abstract: This paper proposes an improved lightweight network for recognizing abnormal behavior among factory personnel based on YOLOv5， to addressing challenges such as complex backgrounds and limited computational resources. This network integrates Omni-dimensional Dynamic Convolution （ODConv） and the Explicit Visual Center Block （EVCBlock）， resulting in improved detection performance while reducing parameter computation. The ODConv module is introduced in the neck network to  enhance the model’s adaptability to complex factory environments and decrease the number of model parameters， while the EVCBlock module is added at the end of the backbone network to improve the detection accuracy of the model and compensate for accuracy loss of model caused by the reduction of parameters. The Normalized Wasserstein Distance （NWD） loss is constructed to optimize the model training process and enhance the model’s detection performance on small targets. Several enhanced detection models are constructed based on existing lightweight methods to compare detection accuracy and parameter count. Results demonstrate that the proposed lightweight recognition model has fewer parameters while maintaining high detection accuracy compared with the existing methods. Compared with the original model， the mAP of the detection model built in this paper increases by 3.2 percentage points and GFLOPs decreases by 2.2. This work is of guiding significance to realize rapid detection and accurate identification of factory personnel’s abnormal behavior in industrial production scenarios.

Key words: Key words：lightweight model, abnormal behavior detection, YOLOv5, omni-dimensional dynamic convolution, explicit visual center