LLE-STD: Traffic Sign Detection Method Based on Low-Light Image Enhancement and Small Target Detection

Tianqi Wang, Hongquan Qu (), Chang’an Liu, Tong Zheng and Zhuoyang Lyu
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Tianqi Wang: School of Electrical and Control Engineering, North China University of Technology, Beijing 100144, China
Hongquan Qu: School of Information Science and Technology, North China University of Technology, Beijing 100144, China
Chang’an Liu: School of Information Science and Technology, North China University of Technology, Beijing 100144, China
Tong Zheng: School of Computer and Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China
Zhuoyang Lyu: Computer Science and Applied Math, Brown University, Providence, RI 02912, USA

Mathematics, 2024, vol. 12, issue 19, 1-22

Abstract: With the continuous development of autonomous driving, traffic sign detection, as an essential subtask, has witnessed constant updates in corresponding technologies. Currently, traffic sign detection primarily confronts challenges such as the small size of detection targets and the complexity of detection scenarios. This paper focuses on detecting small traffic signs in low-light scenarios. To address these issues, this paper proposes a traffic sign detection method that integrates low-light image enhancement with small target detection, namely, LLE-STD. This method comprises two stages: low-light image enhancement and small target detection. Based on classic baseline models, we tailor the model structures by considering the requirements of lightweight traffic sign detection models and their adaptability to varying image qualities. The two stages are then coupled to form an end-to-end processing procedure. During experiments, we validate the performance of low-light image enhancement small target detection, and adaptability to images of different qualities using the public datasets GTSDB, TT-100K, and GLARE. Compared to classic models, LLE-STD demonstrates significant advantages. For example, the mAP results tested on the GLARE dataset show that LLE-STD outperforms RetinaNet by approximately 15%. This research can facilitate the practical application of deep learning-based intelligent methods in the field of autonomous driving.

Keywords: traffic sign detection; low-light image enhancement; small target detection; image quality; deep learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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