LC-YOLO: An Improved YOLOv8-Based Lane Detection Model for Enhanced Lane Intrusion Detection

Lane intrusion detection is an essential component of road safety, as vehicles crossing into lanes without proper signalling can lead to accidents, congestion and traffic violations. In order to overcome these challenges, it has become critical for the future autonomous vehicles and ADAS to possess a precise and reliable lane detection technique which could then further monitor the lane violation in real-time. However, lane detection is still challenging due to variants in lighting conditions, obstructions and weak markers. This research paper proposes a new YOLOv8 architecture for lane detection and traffic monitoring systems. The modifications considered in the paper are the addition of the large separable kernel attention (LSKA) module and the coordinate attention (CA) mechanism, which enhance the model's feature extraction and its performance in various real-world scenarios. Furthermore, a new lane intrusion detection (LID) algorithm was created which effectively distinguishes between actual lane intrusions forbidden ones (e.g., crossing solid lane lines) and permissible ones (e.g., crossing dashed lane lines), a crucial aspect for traffic management. The model was successfully tested by transferring the data which was personally recorded on Chinese highways and that show its function in a real environment. The model was tested using a custom dataset which included videos taken on Chinese highways, demonstrating its ability to work under real-world conditions. In this way, the results show that the proposed YOLOv8 model improves the accuracy and reliability of the lane detection tasks, with the model achieving a mAP of 97.9%, which will be useful and a significant advancement in the application of AI to public safety and highlights the critical role of state-of-the-art deep learning algorithms for enhancing road safety and traffic control.