Application of Deep Learning

The intelligent transportation system has received a lot of attention in recent years. Vehicle detection is an important task in this area where vehicle counting and classification are two important applications. Taking the size of vehicles and illumination factor into consideration the detection remains a challenge. To overcome the above problem we propose aVision-Based Discernment and Counting of Vehicles in Low Illumination Using Deep Learning, In the proposed system, we enhance the low illumination image using the Dual-channel prior-based method. By using the segmentation method the highway road is first extracted and biased into two areas namely remote area and proximal area. Here we use the YOLOv3 network, by placing the above two areas in this network, we can detect the type and position of the vehicle. Finally, by using the ORB algorithm we can judge the direction of the vehicle and obtain a number of different vehicles. The results obtained are such that the accurate count of vehicles especially for small-sized vehicles in low illumination.

Autorentext

Dr. Shafiulla Basha Shaik, Assistenzprofessor, Y.S.R. Engg. College der Yogi Vemana Universität, Proddatur. Y.S.R (Dist), Andhra Pradesh, Indien - 516360, mit über 15 Jahren Lehrerfahrung, hat einen M.Tech in Elektronik und Kommunikationstechnik von der JNTU, Hyderabad, und einen Doktortitel in Biomedizinischer Bildverarbeitung von der Yogi Vemana Universität.