Zhang, Diyi, Zhou, Qinghui et Xie, Yidong.
2025.
« A refined traffic light detection method for challenging low visibility scenarios with CS-YOLOv10 algorithm ».
In Proceedings of the CSME-CFDSC-CSR 2025 International Congress (Montreal, QC, Canada, May 25-28, 2025)
Coll. « Progress in Canadian Mechanical Engineering », vol. 8.
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Résumé
Autonomous vehicles encounter significant challenges in accurately identifying traffic lights under adverse conditions such as fog and rain, due to reduced visibility and the difficulty of detecting small targets. To address these challenges, this paper presents an enhanced algorithm, KCS-YOLO, aimed at improving the detection and recognition of traffic lights in low visibility environments. Initially, we focused on improving the advanced YOLOv10 algorithm. To boost the detection of small targets, we developed the CS-YOLOv10 algorithm by incorporating the CA attention mechanism and creating a dedicated small target detection layer. Additionally, we created a traffic light image dataset and applied the dark channel prior dehazing algorithm for preprocessing to enhance the recognition accuracy and robustness of the proposed algorithm. Finally, the CS-YOLOv10 algorithm was evaluated through comparison and ablation experiments. The results demonstrated that the mAP of the CS-YOLOv10 algorithm reached 98.32%, a 5.68% improvement over the YOLOv10 baseline. These findings confirm that the CS-YOLOv10 algorithm offers high accuracy in object detection and recognition, significantly enhancing traffic light detection for autonomous vehicles in low visibility conditions.
| Type de document: | Compte rendu de conférence |
|---|---|
| Éditeurs: | Éditeurs ORCID Hof, Lucas A. NON SPÉCIFIÉ Di Labbio, Giuseppe NON SPÉCIFIÉ Tahan, Antoine NON SPÉCIFIÉ Sanjosé, Marlène NON SPÉCIFIÉ Lalonde, Sébastien NON SPÉCIFIÉ Demarquette, Nicole R. NON SPÉCIFIÉ |
| Date de dépôt: | 18 déc. 2025 15:34 |
| Dernière modification: | 18 déc. 2025 15:34 |
| URI: | https://espace2.etsmtl.ca/id/eprint/32530 |
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