A Novel Method for Multiple Object Detection on Road Using Improved YOLOv2 Model

Abstract

Object detection is a branch of machine vision and image processing that deals with instances of a certain class of semantic items. One of the most significant habits of object detection in intelligent transportation schemes is vehicle detection. Its aim is to extract clear-cut vehicle-type information from photographs or videos of automobiles. A fully convolutional network (FCN) is employed in sophisticated driver assistance systems for high performance and quick object identification (ADAS). A novel vehicle detection model employing YOLOv2 is presented to tackle the difficulties of prevailing vehicle detection, such as the absence of vehicle-type recognition, stumpy detection accuracy and sluggish speed. The detection model is trained using the VOC and COCO datasets, and the detection enactment is evaluated quantitatively using KITTI training pictures. In addition, the performance of the YOLOv2 model was compared to that of prior models.