VEHICLE DETECTION USING DEEP LEARNING

Abstract

The deep learning object detection algorithms have become one of the powerful tools for road vehicle detection in autonomous driving where vehicles are capable of sensing its environment and operating without human involvement where they can go anywhere a traditional car goes and do

everything that an experienced human driver does. However, the limitation of the number of high- quality labeled training samples makes the single-object detection algorithms unable to achieve

satisfactory accuracy in road vehicle detection. In this paper, by comparing the pros and cons of various object detection algorithms, an ensemble is attenuated with multiple models and they are selected for a method where the first step groups the overlapping regions. Subsequently, a voting strategy is applied to discard some of those groups, this can further reduce the vehicle misdetection of the target detection algorithm, helps in obtaining a better detection result with a Non-maximum Suppression algorithm for the final prediction.