Workshop Safety Helmet Wearing Detection Model Based on SCM-YOLO

• Published in: Sensors (Basel, Switzerland) Vol. 22; no. 17; p. 6702
• Main Authors: Zhang, Bin, Sun, Chuan-Feng, Fang, Shu-Qi, Zhao, Ye-Hai, Su, Song
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 05.09.2022; MDPI
• Subjects: Accuracy; Algorithms; Conferences, meetings and seminars; convolutional block attention module; Deep learning; K-Means++ clustering algorithm; label smoothing; Safety and security measures; safety helmet wearing detection; Semantics; spatial pyramid pooling structure; Telecommunication systems; YOLOv4-tiny
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s22176702

In order to overcome the 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, 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.