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 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%.