Computer Science ›› 2023, Vol. 50 ›› Issue (11A): 220700147-8.doi: 10.11896/jsjkx.220700147

• Image Processing & Multimedia Technology • Previous Articles     Next Articles

YOLOv3 Vehicle Recognition Algorithm for Adaptive Dehazing in Complex Environments

YANG Xiuzhang1, WU Shuai2, LI Na3, YANG Wenwen4, LIAO Wenjing1, ZHOU Jisong1   

  1. 1 School of Information,Guizhou University of Finance and Economics,Guiyang 550025,China
    2 School of Information Management,Nanjing Agricultural University,Nanjing 210003,China
    3 Systems Engineering Research Institute,Beijing 100094,China
    4 Shaanxi Library,Xi'an 710000,China
  • Published:2023-11-09
  • About author:YANG Xiuzhang,born in 1991,Ph.D.His main research interests include artificial intelligence,image identification and natural language processing.
    WU Shuai,born in 1994,Ph.D candidate.His main research interests include information service and computer application.
  • Supported by:
    Guizhou Science and Technology Plan Project(qiankehe foundation [2020]1Y279) and Guizhou University of Finance and Economics Scientific Research Fund Project(2021KYQN03).

Abstract: In view of the complex environmental factors will seriously affect the performance of road vehicle target detection algorithms,traditional methods have low recognition accuracy and slow perception,which seriously threatens traffic safety.This paper proposes a YOLOv3 vehicle recognition algorithm based on adaptive image dehazing.First,an adaptive image dehazing algorithm is constructed in the image preprocessing stage.The ACE dehazing algorithm and the dark channel dehazing algorithm are combined to effectively reduce the noise of rain and fog images.Second,the improved YOLOv3 algorithm is used to identify and locate the car position.Finally,the effectiveness of the method is demonstrated through detailed comparative experiments,and the vehicles driving in complex weather are accurately identified.Experimental results show that the proposed method can effectively reduce the noise in rain and fog conditions,and can effectively locate the driving vehicle.Its precision,recall and F1 value is 0.944,0.934 and 0.939,respectively,which is higher than that of traditional SSD,YOLO and YOLOv3 algorithms.It has good robustness and speed,which will provide theoretical basis for the development of intelligent transportation and has practical significance.

Key words: Deep learning, Adaptive image dehazing, YOLOv3, Vehicle recognition, Target detection

CLC Number: 

  • TP391.4
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