A Modified YOLO Model for On-Road Vehicle Detection in Varying Weather Conditions

Abstract

Vision-based automatic detection and tracking of on-road vehicles is a demanding research area in intelligent transportation systems (ITS). Several investigation reports are available in the literature in this research field using various machine learning (ML) techniques. This article proposes a method for on-road vehicle detection and tracking in varying weather conditions by modifying the structure of you only look once (YOLO), a recently developed deep learning model in computer vision for detecting objects. ML-based systems repurpose classifiers to detect the vehicles, whereas in YOLO model, a single convolutional neural network (CNN) predicts the detecting bounding boxes and the class probabilities for those boxes together in one evaluation from the full images. As the entire detection is performed using a single network, the detection process can be viewed as an end-to-end system. In this work, 16 convolutional layers of YOLO have been used along with two fully connected layers at the end. The vehicles have been detected in varying weather conditions. Two different public data sets, namely CDNet 2014 and LISA 2010 data sets have been used to evaluate the system performance, and it outperforms state-of-the-art results.