Computer Science

Computer Science ›› 2025, Vol. 52 ›› Issue (11A): 250200051-7.doi: 10.11896/jsjkx.250200051

• Image Processing & Multimedia Technology • Previous Articles     Next Articles

Unmanned Driving Scene Object Detection Method Based on Local Features and Feature Fusion

JI Tao1,2,3, YANG Yifang1,2, FENG Yachun2, WU Lingfan2, LI Xuliang2, LI Yawei2   

  1. 1 School of Information Science,Yunnan Normal University,Kunming 650500,China
    2 School of Astronautics,Beihang University,Beijing 102206,China
    3 Southwest United Graduate School,Kunming 650500,China
  • Online:2025-11-15 Published:2025-11-10
  • About author:JI Tao,born in 1999,postgraduate.His main research interests include object detection and embedded system.
    YANG Yifan,born in 1986,Ph.D,associate professor,master supervisor.His main research interests include embedded edge intelligent computing,image enhancement,object recognition and tracking.
  • Supported by:
    National Natural Science Foundation of China(62476017).

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Abstract: In the context of unmanned driving,the accuracy and robustness of object detection are of vital importance to the performance of the system.Aiming at the false detection and missed detection phenomena that occur when existing deep learning-based network models deal with small objects and occluded objects inunmanned driving scenarios,an LSDA-YOLO network model is proposed.Firstly,the LocalSimAM attention mechanism is proposed to address the issue of information loss,and it is applied to the Backbone.Meanwhile,the SHSA attention mechanism is introduced,and an information aggregation network is designed to enhance the detection ability for occluded objects.In the Neck part,by dynamically adjusting the upsampling ratio,the adaptability of the model to multi-scale features is enhanced,reducing the missed detection rate of small objects.In the Head part,the ASFF strategy is introduced to enhance the model’s multi-scale detection ability.Experimental results show that the LSDA-YOLO network model improves the mAP0.5 and mAP0.5:0.95 by 3.1 percentage points and 3.9 percentage points respectively on the KITTI dataset,outperforming the YOLOv11n baseline network model,and is suitable for high-precision real-time detection in unmanned driving scenarios.

Key words: Attention mechanism, Unmanned driving, Vehicle detection, Pedestrian detection, Feature fusion

CLC Number: 

  • TP391.41
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