THI-YOLO: Improved non-motorized drivers helmet detection of YOLOv8

Sun Guangling; Wang Xinbo

doi:10.12086/oee.2024.240244

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Sun G L, Wang X B. THI-YOLO: Improved non-motorized drivers helmet detection of YOLOv8[J]. Opto-Electron Eng, 2024, 51(12): 240244. doi: 10.12086/oee.2024.240244

Citation:

Sun G L, Wang X B. THI-YOLO: Improved non-motorized drivers helmet detection of YOLOv8[J]. Opto-Electron Eng, 2024, 51(12): 240244. doi: 10.12086/oee.2024.240244

THI-YOLO: Improved non-motorized drivers helmet detection of YOLOv8

Sun Guangling^1,2, ,,
Wang Xinbo^1,

1.
School of Electronic and Information Engineering, Anhui Jianzhu University, Hefei, Anhui 230601, China
2.
Anhui Interational Joint Research Center for Intelligent Perception and High-dimensional Modeling of Ancient Buildings, Anhui Jianzhu University, Hefei, Anhui 230601, China

Fund Project: Project supported by National Natural Science Foundation of China (62001004), China Construction Education Association Education and Teaching Research Projects (2023069)，2023 Anhui Province's Housing, Urban-Rural Development Science and Technology Plan Project (2023-YF058, 20123-YF113), Key Project of Scientific Research in Anhui Province (2023AH050164), and Outstanding Youth Research Program for Universities in Anhui Province (2023AH020022)

More Information

^*Corresponding author: sunguangling@163.com
CSTR: 32245.14.oee.2024.240244

Received Date 18 October 2024

Revised Date 15 November 2024

Accepted Date 16 November 2024

Published Date 25 December 2024

Abstract

Abstract

Aiming at the phenomenon that complex background often exists in non-motorized drivers' helmet detection and the diversity of detection target scales often exists in the detection scene, which in turn leads to the low detection efficiency and misdetection and omission, a YOLOv8 algorithm oriented to the detection of traffic helmets is proposed. Combining the advantages of GSConv and CBAM in the C2f module, the C2f_BC module is designed to effectively improve the feature extraction capability of the model while reducing the number of model parameters. A multi-core parallel perception network (MP-Parnet) is designed to improve the model's perception and feature extraction ability for multi-scale targets so that it can be better applied to complex scenes. To alleviate the problem of positive and negative sample imbalance in complex scenes, Focaler-IoU is introduced based on the original model's loss function CIoU, and a threshold parameter is introduced to improve the calculation of the IoU loss, thus alleviating the phenomenon of positive and negative sample imbalance,and effectively improves the model's accuracy of target frame localization in complex background. The experimental results show that compared with the original model, the improved YOLOv8n maintains a decrease in the number of parameters while the mAP50 and mAP50: 95 increase by 2.2% and 1.9% on the self-built dataset Helmet, and 1.8% and 1.9% on the open-source dataset TWHD, which suggests that the improved model can be better applied to the helmet detection of non-motorized drivers in the scenario.
- object detection /
- YOLOv8 /
- attention mechanism /
- loss function /
- feature extraction

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References

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Overview

Overview

With the gradual emergence of computer vision in recent years, target detection algorithms are also constantly innovating and developing, and the detection of safety helmets on two-wheeled vehicles is also one of the focuses of research scholars. Two-wheeled vehicles have become the main means of transportation for citizens to travel at this stage, but the phenomena of citizens running red lights and not wearing safety helmets are still common, so it is especially urgent to design a helmet-wearing detection method for cyclists. At the present stage of safety helmet detection, there are still some difficulties, such as complex background information, the detection target exists in different scales of the diversity of changes, so the design of higher performance helmet detection algorithms needs to carry out further research. Aiming at the phenomenon that complex background often exists in non-motorized drivers' helmet detection and the diversity of detection target scales often exists in the detection scene, which in turn leads to low detection efficiency and misdetection and omission, a YOLOv8 algorithm oriented to traffic helmet detection is proposed. The alteration points of this paper mainly contain the following two blocks. The first is the C2f_BC module, the GSConv module with the idea of combining group convolution and spatial convolution is introduced, and the attention mechanism combining channel and space (convolutional block attention module, CBAM) is introduced in Bottleneck in C2f. To effectively reduce the computational complexity and enhance the extraction of local and global features, we designed the parallel multiscale feature fusion module MP-Parnet (parallel multiscale perception networks) and redesigned the Parnet (parallel networks) by using the parallel depth-separable feature fusion module with different scales. The second is a parallel depthwise separable convolution (DWConv) kernel for different scales. It is used instead of the ordinary convolution of the original module, which effectively adapts to the acquisition ability of different scales of targets. 3. Focaler-IoU is introduced into the original model, and Focaler-CIoU is designed, which effectively enhances the performance of the detection model in both classification and detection. detection performance. The experimental results show that compared with the original model, the improved YOLOv8n maintains a decrease in the number of parameters while the mAP50 and mAP50: 95 increase by 2.2% and 1.9% on the self-built dataset Helmet, and 1.8% and 1.9% on the open-source dataset TWHD, which suggests that the improved model can be better applied to the helmet detection of non-motorized drivers in the scenario.