Enhanced feature extraction YOLO industrial small object detection algorithm based on receptive-field attention and multi-scale features

Abstract

Abstract To guarantee the stability and safety of industrial production, it is necessary to regulate the behavior of employees. However, the high background complexity, low pixel count, occlusion and fuzzy appearance can result in a high leakage rate and poor detection accuracy of small objects. Considering the above problems, this paper proposes the EFE-YOLO (Enhanced feature extraction-You Only Look Once) algorithm to improve the detection of industrial small objects. To enhance the detection of fuzzy and occluded objects, the PSRFA (PixelShuffle and Receptive-Field Attention) upsampling module is designed to preserve and reconstruct more detailed information and extract the receptive-field attention weights. Furthermore, the MSE (multi-scale and efficient) downsampling module is designed to merge global and local semantic features to alleviate the problem of false and missed detection. Subsequently, the AFAF (Adaptive Feature Adjustment and Fusion) module is designed to highlight the important features and suppress background information that is not beneficial for detection. Finally, the EIoU loss function is used to improve the convergence speed and localization accuracy. All experiments are conducted on homemade dataset. The improved YOLOv5 algorithm proposed in this paper improves mAP@0.50 (mean average precision at a threshold of 0.50) by 2.8\% compared to the YOLOv5 algorithm. The average precision and recall of small objects show an improvement of 8.1\% and 7.5\%, respectively. The detection performance is still leading in comparison with other advanced algorithms.