Lane Detection on Rainy Nights Based on Memory and Discretization Mechanisms

The reflections of multi-colored lights involved on rainy nights present strong uncertainties and abruptness, resulting in a high rate of false and missed detections in existing methods. To solve this issue, this paper proposes a lane detection method based on memory and discretization mechanisms. F...

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Vydané v:	IEEE transactions on intelligent transportation systems Ročník 26; číslo 7; s. 9529 - 9541
Hlavní autori:	Li, Yonghang, Wang, Chang, Wang, Yifei, Ren, Miao, Niu, Jin, Zhao, Jikang, Du, Kai
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	IEEE 01.07.2025
Predmet:	Accuracy discretization mechanism Feature extraction Filters Heuristic algorithms Image edge detection Lane detection memory mechanism Optimization rainy night Real-time systems Reflection Roads
ISSN:	1524-9050, 1558-0016
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Abstract	The reflections of multi-colored lights involved on rainy nights present strong uncertainties and abruptness, resulting in a high rate of false and missed detections in existing methods. To solve this issue, this paper proposes a lane detection method based on memory and discretization mechanisms. Firstly, a Memory Fruit-fly-optimizer with Individual Differences (MFID) is innovatively proposed to drive Multi-threshold Otsu (MOtsu)-based multi-class segmentation of lanes, which is a high-dimensional optimization task with real-time and local optimal challenges, for capturing lane clues obscured in multi-intensity reflections, consequently reducing missed detections. Specifically, to solve the challenges inherent in the task, the MFID incorporates a novel memory mechanism to establish fast-converging initial conditions for real-time detection, while creatively considering individual differences to motivate multi-swarm optimization that mitigates local optima risks. After integration, the MFID-MOtsu is constructed for lane segmentation. Subsequently, a dynamic discretization mechanism is proposed to efficiently separate lane edges from interference edges, mitigating accuracy degradation caused by their entanglement. Finally, the false detection issue is greatly reduced through the implementation of adaptive geometric filters. The experimental results demonstrate that the proposed method achieves an average accuracy of 93.21% on rainy nights, indicating an average improvement of 12.7% over state-of-the-art methods. Additionally, without any parameter modifications, the proposed method is applicable to both normal and classic challenging scenes, such as nights, tunnels, rainy days, and shadows. The algorithm achieves an average accuracy of 96.2% and an average detection speed of 46 frames per second.
AbstractList	The reflections of multi-colored lights involved on rainy nights present strong uncertainties and abruptness, resulting in a high rate of false and missed detections in existing methods. To solve this issue, this paper proposes a lane detection method based on memory and discretization mechanisms. Firstly, a Memory Fruit-fly-optimizer with Individual Differences (MFID) is innovatively proposed to drive Multi-threshold Otsu (MOtsu)-based multi-class segmentation of lanes, which is a high-dimensional optimization task with real-time and local optimal challenges, for capturing lane clues obscured in multi-intensity reflections, consequently reducing missed detections. Specifically, to solve the challenges inherent in the task, the MFID incorporates a novel memory mechanism to establish fast-converging initial conditions for real-time detection, while creatively considering individual differences to motivate multi-swarm optimization that mitigates local optima risks. After integration, the MFID-MOtsu is constructed for lane segmentation. Subsequently, a dynamic discretization mechanism is proposed to efficiently separate lane edges from interference edges, mitigating accuracy degradation caused by their entanglement. Finally, the false detection issue is greatly reduced through the implementation of adaptive geometric filters. The experimental results demonstrate that the proposed method achieves an average accuracy of 93.21% on rainy nights, indicating an average improvement of 12.7% over state-of-the-art methods. Additionally, without any parameter modifications, the proposed method is applicable to both normal and classic challenging scenes, such as nights, tunnels, rainy days, and shadows. The algorithm achieves an average accuracy of 96.2% and an average detection speed of 46 frames per second.
Author	Wang, Yifei Du, Kai Wang, Chang Ren, Miao Zhao, Jikang Li, Yonghang Niu, Jin
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Snippet	The reflections of multi-colored lights involved on rainy nights present strong uncertainties and abruptness, resulting in a high rate of false and missed...
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SubjectTerms	Accuracy discretization mechanism Feature extraction Filters Heuristic algorithms Image edge detection Lane detection memory mechanism Optimization rainy night Real-time systems Reflection Roads
Title	Lane Detection on Rainy Nights Based on Memory and Discretization Mechanisms
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