Road Traffic Object Detection Based on Improved YOLOV11m Algorithm

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The YOLOv11mAEA algorithm achieves a precision of 76.1% and mAP of 58.7%, surpassing the baseline model.
Inclusion of the Attention-based Intra-scale Feature Interaction module raises the algorithm's mAP by 1.6%.
Integrating the Auxiliary Head module improves overall network performance and detection accuracy.
Improvements in detection of occluded objects emphasize the algorithm's applicability in real-world traffic scenarios.

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Algorithm improves precision and mean average precision in traffic object detection, indicating better performance in complex environments.

You et al. (2025) studied this question.

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Algorithm improves precision and mean average precision in traffic object detection, indicating better performance in complex environments.