Abstract: Convolutional neural networks (CNNs) were widely used in object detection tasks. Usually, CNNs with strong object detection performance were difficult to apply to small, mobile embedded systems with limited computational resources due to the large number of parameters. Aiming at this problem, the lightweight improvement method for the safety helmet object detection task based on YOLOv7 has been studied. The first step was the lightweight improvement of the network. Taking YOLOv7 and YOLOv7-Tiny as the basic networks, respectively, the backbone network was improved using the MobileOne network. YOLOv7-MobileOne (YOLOv7-MO) and YOLOv7-Tiny-MobileOne (YOLOv7-TMO) were obtained. Compared with the original network parameters, the number of parameters decreased by 36.8% and 37.9%, respectively. Verified on the Pascal VOC dataset, the YOLOv7-MO had a 3.7% decrease in mAP @.5 compared to the YOLOv7. The YOLOv7-MO had a 9.8% increase in mAP @.5 compared to the YOLOv7-TMO. The second step was to improve the detection accuracy. The Coordinate Attention (CA) module was integrated at different positions of YOLOv7-MO and YOLOv7-TMO, respectively, to obtain YOLOv7-MO-Coordinate Attention (YOLOv7-MOC) and YOLOv7-TMO-Coordinate Attention (YOLOv7-TMOC). Verified on the Pascal VOC dataset, YOLOv7-MOC improved 1.44% compared to YOLOv7-MO's mAP @.5 and reduced FPS by 5.4Hz. Verified on the self-constructed two-wheeled cyclists helmet dataset (TCHD), YOLOv7-MOC increased by 0.8% compared to YOLOv7-MO's mAP @.5 and reduced FPS by 0.3Hz. YOLOv7-MOC increased by 1.0% compared to YOLOv7's mAP @.5 to 77.1%. The corresponding FPS was 28.7Hz higher, reaching 89.3Hz. Finally, experiments were conducted using the Raspberry Pi 4B embedded development board, based on the Linux system and the Pytorch framework, with the YOLOv7-TMOC network model. The results proved that the improved network model can be applied to the object detection of small embedded systems.

Xufei Wang, Penghui Wang, Zishuo Wang, Jeonyoung Song and Jinde Song. “An Improved Method Based on YOLOv7 for Detecting the Safety Helmets of Two-Wheeled Bicycle Riders”. International Journal of Advanced Computer Science and Applications (IJACSA) 16.11 (2025). http://dx.doi.org/10.14569/IJACSA.2025.0161116

@article{Wang2025,
title = {An Improved Method Based on YOLOv7 for Detecting the Safety Helmets of Two-Wheeled Bicycle Riders},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0161116},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0161116},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {11},
author = {Xufei Wang and Penghui Wang and Zishuo Wang and Jeonyoung Song and Jinde Song}
}