Object Detection Method in Traffic by On-Board Computer Vision with Time Delay Neural Network

Abstract

A collision avoidance system play an important role in reducing incidents occurring on the road, which the object detection is crucial to enable obstacle avoidance for this system. The objective of this paper is to improve general object detection methods for vehicles in order to prevent a collision of the vehicles and the obstacles - of which we do not know the exact shape, size or color. A combined computer vision system with artificial neural networks can improve the performance of the vehicle has the ability to see and recognize the obstacles like human beings. In this paper, the authors present the algorithm for vehicles to detect general objects, which can classify obstacles that are real obstacles or fake obstacles, such as a painting or text on the road. The proposed method, we combined on-board computer vision system based on Histograms of Oriented Gradient (HOG) and Time Delay Neural Network (TDNN). We extract feature of the obstacles by HOG and using TDNN to recognize and classify the obstacles. The experimental results showed that this system can detect general objects, and is not restricted to vehicles, objects or pedestrians. It has provided good results along with high accuracy and reliability, which it is accurate enough to provide a warning to the driver when a collision is imminent.