28 June 2022 Exposure consistency for lane detection under varied light conditions
Mingjing Yang, Yingdong Wei, Lin Pan, Liqin Huang
Author Affiliations +
Abstract

Lane detection is challenging under varied light conditions (e.g., night, shadow, and dazzling light) because a lane becomes blurred and extracting features becomes more difficult. Some researchers have proposed methods based on multitask learning and contextual information to solve this problem; however, these methods result in additional computing. A data enhancement method based on retinex theory is proposed. This method improves the adaptability of a lane model under varied light conditions. In particular, we design an image enhancement network for calculating the reflectivity of images, modifying their exposure, and then generating images with consistent exposure. These images are fed to the lane detection model for training and detection. Our network consists of two parts: exposure-consistent image generation and lane detection. We validate our method on the CULane dataset, and results show that it can improve lane detection performance, particularly on light-related datasets.

© 2022 SPIE and IS&T 1017-9909/2022/$28.00 © 2022 SPIE and IS&T
Mingjing Yang, Yingdong Wei, Lin Pan, and Liqin Huang "Exposure consistency for lane detection under varied light conditions," Journal of Electronic Imaging 31(3), 030502 (28 June 2022). https://doi.org/10.1117/1.JEI.31.3.030502
Received: 16 February 2022; Accepted: 7 June 2022; Published: 28 June 2022
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KEYWORDS
Image enhancement

Reflectivity

Image processing

Performance modeling

Convolution

Data modeling

Visualization

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