# 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.

## Key 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.

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Source: https://tomesphere.com/paper/1905.04740