无线电工程2025，Vol.55Issue(5)：928-937,10.DOI:10.3969/j.issn.1003-3106.2025.05.004
基于多尺度特征融合的复杂交通场景目标检测算法
Object Detection Algorithm for Complex Traffic Scenes Based on Multi-scale Feature Fusion
摘要
Abstract
Object detection in road scenes is crucial for intelligent transportation systems and autonomous driving,but complex traffic conditions pose significant challenges.Aiming at the problems of existing detection algorithms such as the diversity of object scales,the interference of the surrounding background leading to misdetection and omission,as well as the accuracy degradation due to occlusion,etc.,a Multi-Scale Feature Fusion(MSFF)-based object detection algorithm for autonomous driving is proposed.Firstly,the C2f-RepViT module is constructed in the Backbone network to generate more expressive feature representations,in addition,the backbone is optimized by the MSFF module to capture the image details and contextual information accurately.Secondly,the Feature Bidirectional Diffusion Pyramid Network(FBDPN)structure is designed in the Neck layer to improve the effect of MSFF significantly.Finally,the PIoU(Powerful-IoU)is introduced to enhance the anchor box quality evaluation capability,accelerating the convergence speed of the model and improving the accuracy.The experimental results on the KITTI dataset show that compared with the original YOLOv8 algorithm,the proposed detection algorithm improves the precision by 2.2％,recall by 1.9％,mAP@0.5 by 2.1％,and mAP@0.5:0.95 by 1.6％,which proves that the proposed algorithm achieves a better detection accuracy and effect in autonomous driving scenarios.关键词
自动驾驶/目标检测/YOLOv8/多尺度特征融合/损失函数Key words
autonomous driving/object detection/YOLOv8/MSFF/loss function分类
计算机与自动化引用本文复制引用
董善,陈清江..基于多尺度特征融合的复杂交通场景目标检测算法[J].无线电工程,2025,55(5):928-937,10.基金项目
国家自然科学基金(12373032)National Natural Science Foundation of China(12373032) （12373032）
