BT-YOLO11: Automatic Driving Road Target Detection in Complex Scenarios
Due to the problems faced by autonomous driving scenarios in complex scenarios, generalized target detectors are challenged, especially the detection accuracy of occluded and small targets. In order to solve this problem, a target detection algorithm for complex scenes BT-YOLO11 is proposed based on...
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10971358/ |
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| author | Qi Wang Qi Long Wang |
| author_facet | Qi Wang Qi Long Wang |
| author_sort | Qi Wang |
| collection | DOAJ |
| description | Due to the problems faced by autonomous driving scenarios in complex scenarios, generalized target detectors are challenged, especially the detection accuracy of occluded and small targets. In order to solve this problem, a target detection algorithm for complex scenes BT-YOLO11 is proposed based on the latest YOLO11. In the feature extraction stage, Bi-level Routing Attention is introduced to enhance the model’s ability to capture feature information. The Tri-directional Feature Pyramid Net is used in the feature fusion stage, which adequately fuses different levels of feature information and improves the accuracy and robustness of the algorithm. By introducing an Adaptive Threshold Focus Loss function, the model focuses more on detection targets that are difficult to classify, the model generalization ability is improved. The experimental results show that the improved algorithm exhibits very competitive performance on the KITTI dataset. Specifically, it achieves a mAP50 metric of 95% and 77% under the more challenging mAP50:95 evaluation criterion. Compared to the YOLOv11s model, the algorithm proposed in this study improves the mAP50 by 2.6% and the mAP50:95 by 4.4%. |
| format | Article |
| id | doaj-art-c933ca8fbc684bfc8e23bef87b3fbfc3 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-c933ca8fbc684bfc8e23bef87b3fbfc32025-08-20T03:10:34ZengIEEEIEEE Access2169-35362025-01-0113723647237410.1109/ACCESS.2025.356274710971358BT-YOLO11: Automatic Driving Road Target Detection in Complex ScenariosQi Wang0https://orcid.org/0000-0002-1404-582XQi Long Wang1https://orcid.org/0009-0001-6240-8058College of Computer Science, Nanjing University of Information Science & Technology, Nanjing, ChinaCollege of Computer Science, Nanjing University of Information Science & Technology, Nanjing, ChinaDue to the problems faced by autonomous driving scenarios in complex scenarios, generalized target detectors are challenged, especially the detection accuracy of occluded and small targets. In order to solve this problem, a target detection algorithm for complex scenes BT-YOLO11 is proposed based on the latest YOLO11. In the feature extraction stage, Bi-level Routing Attention is introduced to enhance the model’s ability to capture feature information. The Tri-directional Feature Pyramid Net is used in the feature fusion stage, which adequately fuses different levels of feature information and improves the accuracy and robustness of the algorithm. By introducing an Adaptive Threshold Focus Loss function, the model focuses more on detection targets that are difficult to classify, the model generalization ability is improved. The experimental results show that the improved algorithm exhibits very competitive performance on the KITTI dataset. Specifically, it achieves a mAP50 metric of 95% and 77% under the more challenging mAP50:95 evaluation criterion. Compared to the YOLOv11s model, the algorithm proposed in this study improves the mAP50 by 2.6% and the mAP50:95 by 4.4%.https://ieeexplore.ieee.org/document/10971358/Complex scenariosbi-level routing attentionKITTItri-directional feature pyramid netadaptive threshold focus lossYOLO11 |
| spellingShingle | Qi Wang Qi Long Wang BT-YOLO11: Automatic Driving Road Target Detection in Complex Scenarios IEEE Access Complex scenarios bi-level routing attention KITTI tri-directional feature pyramid net adaptive threshold focus loss YOLO11 |
| title | BT-YOLO11: Automatic Driving Road Target Detection in Complex Scenarios |
| title_full | BT-YOLO11: Automatic Driving Road Target Detection in Complex Scenarios |
| title_fullStr | BT-YOLO11: Automatic Driving Road Target Detection in Complex Scenarios |
| title_full_unstemmed | BT-YOLO11: Automatic Driving Road Target Detection in Complex Scenarios |
| title_short | BT-YOLO11: Automatic Driving Road Target Detection in Complex Scenarios |
| title_sort | bt yolo11 automatic driving road target detection in complex scenarios |
| topic | Complex scenarios bi-level routing attention KITTI tri-directional feature pyramid net adaptive threshold focus loss YOLO11 |
| url | https://ieeexplore.ieee.org/document/10971358/ |
| work_keys_str_mv | AT qiwang btyolo11automaticdrivingroadtargetdetectionincomplexscenarios AT qilongwang btyolo11automaticdrivingroadtargetdetectionincomplexscenarios |