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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2409.03320 |
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| _version_ | 1866910590581080064 |
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| author | Zhang, Jingyu Zhang, Wenqing Tan, Chaoyi Li, Xiangtian Sun, Qianyi |
| author_facet | Zhang, Jingyu Zhang, Wenqing Tan, Chaoyi Li, Xiangtian Sun, Qianyi |
| contents | It is very important to detect traffic signs efficiently and accurately in autonomous driving systems. However, the farther the distance, the smaller the traffic signs. Existing object detection algorithms can hardly detect these small scaled signs.In addition, the performance of embedded devices on vehicles limits the scale of detection models.To address these challenges, a YOLO PPA based traffic sign detection algorithm is proposed in this paper.The experimental results on the GTSDB dataset show that compared to the original YOLO, the proposed method improves inference efficiency by 11.2%. The mAP 50 is also improved by 93.2%, which demonstrates the effectiveness of the proposed YOLO PPA. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_03320 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | YOLO-PPA based Efficient Traffic Sign Detection for Cruise Control in Autonomous Driving Zhang, Jingyu Zhang, Wenqing Tan, Chaoyi Li, Xiangtian Sun, Qianyi Computer Vision and Pattern Recognition Artificial Intelligence It is very important to detect traffic signs efficiently and accurately in autonomous driving systems. However, the farther the distance, the smaller the traffic signs. Existing object detection algorithms can hardly detect these small scaled signs.In addition, the performance of embedded devices on vehicles limits the scale of detection models.To address these challenges, a YOLO PPA based traffic sign detection algorithm is proposed in this paper.The experimental results on the GTSDB dataset show that compared to the original YOLO, the proposed method improves inference efficiency by 11.2%. The mAP 50 is also improved by 93.2%, which demonstrates the effectiveness of the proposed YOLO PPA. |
| title | YOLO-PPA based Efficient Traffic Sign Detection for Cruise Control in Autonomous Driving |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2409.03320 |