Pedestrian detection under weather conditions using conditional generative adversarial network

Abstract

<p>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.</p>

Object Detection in Autonomous Vehicles under Adverse Weather: A Review of Traditional and Deep Learning Approaches

“…Moreover, the goal of [140] was to enhance self-driving vehicle detection in adverse weather using YOLOv5 with Transformer and CBAM modules, achieving an impressive mAP of 94.7% and FPS of 199.86. The DL approach proposed in [141] for nighttime vehicle detection in autonomous cars, combining a Generative Adversarial Network for image translation and YOLOv5 for detection, achieved a high accuracy of 96.75%, significantly enhancing the reliability of AV recognition models for night conditions. This study in [12] presents a DL-based intelligent AV weather-detecting system.…”