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Vehicle counting is an important process in the estimation of road traffic density to evaluate the traffic conditions in intelligent transportation systems. With increased use of cameras in urban centers and transportation systems, surveillance videos have become central sources of data. Vehicle detection is one of the essential uses of object detection in intelligent transport systems. Object detection aims at extracting certain vehicle-related information from videos and pictures containing vehicles. This form of information collection in intelligent systems is faced with low detection accuracy, inaccuracy in vehicle type detection, slow processing speeds. In this research, we propose a vehicle detection system from infrared images using YOLO (You Look Only Once) computational mechanism. The YOLO mechanism can apply different machine or deep learning algorithms for accurate vehicle type detection. In this study we propose an infrared based technique to combine with YOLO for vehicle detection in traffic. This method will be compared with a machine learning technique of K-means++ clustering algorithm, a deep learning mechanism of multitarget detection and infrared imagery using convolutional neutral network

Detection of vehicle with Infrared images in Road Traffic using YOLO computational mechanism