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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Aïcha Sahel
University Hassan II Casablanca
Computer Science & IT Engineering

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Pedestrian detection under weather conditions using conditional generative adversarial network Mohammed Razzok; Abdelmajid Badri; Ilham EL Mourabit; Yassine Ruichek; Aïcha Sahel
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 4: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i4.pp1557-1568

Abstract

Nowadays, many pedestrians are injured or killed in traffic accidents. As a result, several artificial vision solutions based on pedestrian detection have been developed to assist drivers and reduce the number of accidents. Most pedestrian detection techniques work well on sunny days and provide accurate traffic data. However, detection decreases dramatically in rainy conditions. In this paper, a new pedestrian detection system (PDS) based on generative adversarial network (GAN) module and the real-time object detector you only look once (YOLO) v3 is proposed to mitigate adversarial weather attacks. Experimental evaluations performed on the VOC2014 dataset show that our proposed system performs better than models based on existing noise reduction methods in terms of accuracy for weather situations.
Co-Authors Abdelmajid Badri Ilham EL Mourabit Mohammed Razzok Yassine Ruichek