@article{ref1,
title="Workshop safety helmet wearing detection model based on SCM-YOLO",
journal="Sensors (Basel)",
year="2022",
author="Zhang, Bin and Sun, Chuan-Feng and Fang, Shu-Qi and Zhao, Ye-Hai and Su, Song",
volume="22",
number="17",
pages="e6702-e6702",
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.<p /> <p>Language: en</p>",
language="en",
issn="1424-8220",
doi="10.3390/s22176702",
url="http://dx.doi.org/10.3390/s22176702"
}