Lane Detection on Rainy Nights Based on Memory and Discretization Mechanisms

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.