Author(s)

Xinghe Chen ¹, Guanchen Du ²

Affiliation(s)

¹ School of Computer Science and Engineering, North Minzu University, Yinchuan, 750021, China
² College of Mathematics and Computer Science, Shantou University, Shantou, 515000, China

Corresponding Author

Xinghe Chen

ABSTRACT

In intricate traffic environments, traffic lights, as pivotal signaling tools, are influenced by factors such as observational distance and lighting conditions. This article proposes an enhanced YOLOv8 model that integrates a hybrid attention mechanism to adapt signal light recognition to complex traffic scenarios. Particularly, the introduction of the Global Attention Mechanism (GAM) within the YOLOv8 model is highlighted. GAM leverages a three-dimensional arrangement and dual-layer MLPs (Multilayer Perceptrons) to emphasize and strengthen channel features that are advantageous for the task of traffic light detection, while also maintaining cross-dimensional channel-spatial dependencies. It concentrates and merges spatial information with channel information through convolutional layers, enabling interaction and avoiding information loss by excluding max-pooling operations. Experimental results demonstrate the exceptional signal light recognition capabilities of the YOLOv8 model enhanced by the GAM attention mechanism in complex traffic scenes, fulfilling practical application requirements across all metrics. Post enhancement, the average recognition rate (Map@50) reaches as high as 93%, demonstrating the model's stability and efficiency in complex environments. The proposed method, based on the improved YOLOv8 model combined with the GAM attention mechanism for signal light recognition, effectively enhances the accuracy and robustness of traffic light detection in complex traffic environments, offering valuable research findings for the advancement and implementation of intelligent transportation systems.

KEYWORDS

Object detection, YOLOv8, attention mechanisms, traffic signals

CITE THIS PAPER

Xinghe Chen, Guanchen Du, Enhancing the application of signal light recognition for the YOLOv8 model in complex traffic scenarios. Advances in Computer, Signals and Systems (2024) Vol. 8: 98-104. DOI: http://dx.doi.org/10.23977/acss.2024.080414.

REFERENCES

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[2] Liu W, Wang Z, Zhou B, et al. Real-time signal light detection based on yolov5 for railway[C]//IOP Conference Series: Earth and Environmental Science. IOP Publishing, 2021, 769(4): 042069.
[3] Wu Q, Guozhong W, Guoping L I. Improved YOLOV5 traffic light real-time detection robust algorithm[J]. Journal of Frontiers of Computer Science & Technology, 2022, 16(1): 231.
[4] Li Z, Zhang W, Yang X. An Enhanced Deep Learning Model for Obstacle and Traffic Light Detection Based on YOLOv5 [J]. Electronics, 2023, 12(10): 2228.
[5] Terven J, Córdova-Esparza D M, Romero-González J A. A comprehensive review of yolo architectures in computer vision: From yolov1 to yolov8 and yolo-nas[J]. Machine Learning and Knowledge Extraction, 2023, 5(4): 1680-1716.
[6] Liu Y, Shao Z, Hoffmann N. Global attention mechanism: Retain information to enhance channel-spatial interactions [J]. arXiv preprint arXiv:2112.05561, 2021.(GAM).

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