Pedestrian detection under weather conditions using conditional generative adversarial network

<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 Most techniques work well sunny days provide accurate data. However, decreases dramatically rainy conditions. In this paper, new system (PDS) generative adversarial network (GAN) module real-time object detector you only look once (YOLO) v3 is proposed mitigate weather attacks. Experimental evaluations performed VOC2014 dataset show that our performs better than models existing noise reduction methods terms accuracy for situations.</p>