FM-YOLOv7: an improved detection method for mine personnel helmet

Abstract

.Due to the complex underground environment and the small object of the helmet, the detection accuracy is low when the original YOLOv7 algorithm is used to detect whether the mine personnel wears the helmet, which cannot be applied to the actual operation site. In response to this problem, we proposed an FM-YOLOv7 mine personnel helmet detection. First, to improve the feature extraction ability of the shallow network and enhance the representation ability of the model on the helmet, we propose the fused-MBCA (fused-MBConv with the coordinate attention) module. Second, to improve the detection ability of small objects, enable the fused features to obtain high-level semantic information and low-level details from different scales, and have more extensive receptive fields, we propose the multi-scale feature fusion efficient layer aggregation networks. Finally, to accelerate the convergence of the model and improve the regression accuracy, we use efficient intersection over union as the bounding box regression loss function. These experiments are based on the self-built mine personnel safety helmet dataset. The results show that the FM-YOLOv7 model outperformed the other six algorithms. The mAP@0.5 of the proposed model can reach 85.7%, which is 1.4% higher than the original YOLOv7 model. Also, the improved YOLOv7 model achieves 91 frames per second in detection speed, which detects whether the mine personnel wears a safety helmet in real time.

CN-YOLO: cascaded network based defect detection approach for insulator aerial images with complex background

“…6,7 Compared with the traditional detection technology, the insulator detection algorithm based on deep learning has a stronger generalization ability and can learn the characteristics of insulators autonomously through the network, which improves the defect detection precision of insulators. However, conventional detection algorithms: typical two-stage detection algorithms, such as R-CNN, 8 fast R-CNN, 9 faster R-CNN, 10 and single-stage detection algorithms, such as SSD, 11 CenterNet, 12 ResNeSt, 13 and you only look once (YOLO [14][15][16] ) series have poor detection effect in images with complex background. Therefore, some researchers have proposed to improve the two-stage detection algorithm based on fast R-CNN.…”