ArticleSensors (Basel, Switzerland)2022
Workshop Safety Helmet Wearing Detection Model Based on SCM-YOLO.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- YOLO-AFL: A Novel Lightweight Algorithm for Real-Time Safety Helmet Detection in Factory Workshops.Sensors (Basel, Switzerland) · 2026Article
- An Improved YOLOv9 Based Object Detection with Attention Mechanism for Personal Protective Equipment.Sensors (Basel, Switzerland) · 2026Article
- Study on the Recognition of Coal Miners' Unsafe Behavior and Status in the Hoist Cage Based on Machine Vision.Sensors (Basel, Switzerland) · 2023Article
- Improved YOLOv4-tiny based on attention mechanism for skin detection.PeerJ. Computer science · 2023Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
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.
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Registered trials
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