Abstract: Deep learning algorithms have made significant progress in the field of computer vision in recent years,but the accuracy of object detection in complex traffic scenes is still unsatisfactory due to the small size of traffic objects,inconspicuous feature,and susceptibility to interference.To address this problem,this paper improves the state-of-the-art YOLO11 and designs the ETF-YOLO11n based on multi-scale feature fusion.Firstly,it designs TFF,which effectively fuses the feature information of different sizes extracted from the backbone.Secondly,it designs HDCFE,effectively integrates the features extracted from different receptive fields and reduces the interference on the detection effect of the model due to occlusion and overlapping.Finally,the proposed GeoCIoU is used to replace CIoU,and the model can provide more accurate feedback on the matching of the predicted box and the ground-truth box through the two different penalization terms.The ETF-YOLO11n achieves an AP of 65.6% and mAP@0.5 of 90.7% on KITTI dataset,which is improved by 2.4 percentage points and 1.2 percentage points.In addition,ETF-YOLO11n achieves 42.5% AP and 59.8% mAP@0.5 on COCO-Traffic,and EFT-YOLOv8n achieves 66.9% AP and 91.5% mAP@0.5 on KITTI dataset.The results show that the proposed methods significantly improve the performance and have good ge-neralization ability to different models and datasets,achieve a good balance between the accuracy and parameters.The source code has been opened.

Key words: Object detection, Multi-feature fusion, Inter over Union, Feature enhancement, Complex traffic scenarios