Workshop Safety Helmet Wearing Detection Model Based on SCM-YOLO

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...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2022-09, Vol.22 (17), p.6702
Main Authors: Zhang, Bin, Sun, Chuan-Feng, Fang, Shu-Qi, Zhao, Ye-Hai, Su, Song
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Clustering
Conferences, meetings and seminars
convolutional block attention module
Data smoothing
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
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Summary: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.
ISSN:1424-8220
1424-8220
DOI:10.3390/s22176702