Work

Traffic light detection for nighttime driving with log-polar transform incorporated learning

Year: 2024

Type: article

Abstract: Recent advancements in advanced driver assistance systems (ADAS) have equipped vehicles with enhanced intelligence. Traffic light detection, a fun- damental function, is crucial for understanding transportation infrastructure. This paper addresses the challenging task of nighttime traffic light detec- tion, including arrow signal recognition. A learning-based approach with a two-stage framework is proposed to tackle nighttime driving scenarios. In the first stage, image intensity encoding and tone-mapping are employed for traffic light detection. Subsequently, an SVM classifier integrated with log- polar transformation is used to recognize arrow signal lights. Due to the scarcity of publicly available traffic light datasets specifically designed for nighttime scenes, a dedicated nighttime traffic light dataset is created for network training and testing. The proposed techniques are evaluated using real-world driving images. With the average accuracy of 97 . 5 % across all traf- fic light categories, the performance evaluation underscores the effectiveness and feasibility of our algorithms for nighttime driving scenarios.

Source: Results in Engineering

DOI: 10.1016/j.rineng.2024.103783

Authors: Huei-Yung Lin, Mu-Yun Tsai

Institutions: National Taipei University of Technology, National Chung Cheng University

Language: English

Topic: Autonomous Vehicle Technology and Safety

Subfield: Automotive Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Sustainable cities and communities

Open Access status: gold

Estimated APC paid: $1,490