Workshop Safety Helmet Wearing Detection Model Based on SCM-YOLO

Workshop Safety Helmet Wearing Detection Model Based on SCM-YOLO

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In order to overcome the click here problems of object detection in complex scenes based on the YOLOv4-tiny algorithm, such as insufficient feature extraction, low accuracy, and low recall rate, an improved YOLOv4-tiny safety helmet-wearing detection algorithm SCM-YOLO is proposed.Firstly, the Spatial Pyramid Pooling (SPP) structure is added after the backbone network of the YOLOv4-tiny model to improve its adaptability of different scale features and increase its effective features extraction capability.Secondly, Convolutional Block Attention Module (CBAM), Mish activation function, K-Means++ clustering algorithm, here label smoothing, and Mosaic data enhancement are introduced to improve the detection accuracy of small objects while ensuring the detection speed.After a large number of experiments, the proposed SCM-YOLO algorithm achieves a mAP of 93.

19%, which is 4.76% higher than the YOLOv4-tiny algorithm.Its inference speed reaches 22.9FPS (GeForce GTX 1050Ti), which meets the needs of the real-time and accurate detection of safety helmets in complex scenes.