Object Detection in Specific Traffic Scenes using YOLOv2

TL;DR

This paper evaluates YOLOv2's effectiveness in real-time object detection within specific traffic scenarios relevant to autonomous driving, demonstrating its practical application and improvements over YOLOv1.

Contribution

It introduces YOLOv2 and explores its application to specific traffic scenes, showcasing its capabilities in real-time detection tasks.

Findings

YOLOv2 performs effectively in traffic scene detection.

Pre-trained YOLOv2 models can be adapted to specific traffic scenarios.

Improvements over YOLOv1 enhance detection accuracy and speed.

Abstract

object detection framework plays crucial role in autonomous driving. In this paper, we introduce the real-time object detection framework called You Only Look Once (YOLOv1) and the related improvements of YOLOv2. We further explore the capability of YOLOv2 by implementing its pre-trained model to do the object detecting tasks in some specific traffic scenes. The four artificially designed traffic scenes include single-car, single-person, frontperson-rearcar and frontcar-rearperson.