Computer Science

Computer Science ›› 2026, Vol. 53 ›› Issue (6A): 250300090-7.doi: 10.11896/jsjkx.250300090

• Image Processing & Multimedia Technology • Previous Articles     Next Articles

Rain and Fog Weather Object Detection Algorithm Based on Improved YOLOv8 Model

ZHANG Shouyi, SHEN Qiang, GUO Yiran, WANG Hanyu   

  1. School of Mechatronics,Beijing Institute of Technology,Beijing 100084,China
  • Online:2026-06-16 Published:2026-06-12
  • About author:ZHANG Shouyi,born in 1999,postgra-duate.His main research interests include artificial intelligence,machine learning,and object detection.
    SHEN Qiang,born in 1977,Ph.D,professor. His main research interests include intelligent unmanned system design, navigation, and control techno-logy.

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Abstract: To address the issue of reduced detection accuracy of traditional object detection algorithms under rainy and foggy weather conditions,this paper proposes an improved YOLOv8-based object detection method.Firstly,the FogEnhanceNet deha-zing enhancement module is introduced to improve the contrast and clarity of target regions at the model input stage,thereby enhancing feature distinguishability.Secondly,an Adaptive Contrast Attention(ACA) mechanism is incorporated to dynamically adjust the weights of channel and spatial information,optimizing target feature representation in low-contrast environments.Finally,a lightweight C2f-Ghost-GF structure is designed to reduce model parameters while leveraging guided filtering(GF) to enhance edge feature extraction for foggy images.Experimental results show that the improved model achieves an 11.3% increase in mAP without a significant increase in model parameters,providing an effective solution for target detection in complex weather conditions.

Key words: Object detection, YOLOv8, Rain and fog weather, Dehazing enhancement, Attention mechanism, Lightweight network, Guided filtering, Edge feature extraction

CLC Number: 

  • TN911.73-34
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