Detection of Small Size Traffic Signs Using Regressive Anchor Box Selection and DBL Layer Tweaking in YOLOv3

Hafsa Amanullah,Waqas Tariq Toor,Yawar Rehman,Manuel Mazzara,Dost Muhammad Saqib Bhatti,Muhammad Ahmad

doi:10.3390/app112311555

Abstract

Traffic sign recognition is a key module of autonomous cars and driver assistance systems. Traffic sign detection accuracy and inference time are the two most important parameters. Current methods for traffic sign recognition are very accurate; however, they do not meet the requirement for real-time detection. While some are fast enough for real-time traffic sign detection, they fall short in accuracy. This paper proposes an accuracy improvement in the YOLOv3 network, which is a very fast detection framework. The proposed method contributes to the accurate detection of a small-sized traffic sign in terms of image size and helps to reduce false positives and miss rates. In addition, we propose an anchor frame selection algorithm that helps in achieving the optimal size and scale of the anchor frame. Therefore, the proposed method supports the detection of a small traffic sign with real-time detection. This ultimately helps to achieve an optimal balance between accuracy and inference time. The proposed network is evaluated on two publicly available datasets, namely the German Traffic Sign Detection Benchmark (GTSDB) and the Swedish Traffic Sign dataset (STS), and its performance showed that the proposed approach achieves a decent balance between mAP and inference time.

Highlights

The recent advancements in technology moved our society towards an intelligent transportation system
The proposed network was evaluated from six different aspects: mAP, precision, recall, false positives, log-average miss rate, and inference time
The network training process can be divided into two steps; first, the Darknet53 network was fixed and the rest of the network was trained for ten epochs with a batch size of thirty-two keeping the learning rate to 1 × 10−3

Summary

Introduction

The recent advancements in technology moved our society towards an intelligent transportation system. This allows humans to delineate the road conditions ahead of time yielding lesser human error and accidents. Driver Assistance Systems (ADAS) such as collision warning, human detection, de-raining systems, and de-hazing systems. The quality of human daily life is improved. The future commercial autonomous driverless cars or intelligent vehicles are likely equipped with self-localization, scene understanding, path planning, and collision avoidance capabilities. A car that can adjust its speed according to the speed limit sign on-road and navigate to its destination safely is in demand. The prime requirement for such cars is an accurate and real-time traffic sign recognition system

Methods

Results

Conclusion