EdgeLane-SEG: an energy-efficient embedded edge AI framework for real-time road marking and lane lines detection with instance segmentation in ADAS and autonomous driving.

Objective: Accurate and energy-efficient perception of road-surface markings is essential for Advanced Driver Assistance Systems (ADAS) and autonomous driving, particularly under real-time embedded constraints. This study proposes EdgeLane-SEG, a unified framework designed to achieve high instance segmentation accuracy while maintaining low computational cost and power consumption on resource-constrained edge platforms. Methods: The proposed framework integrates two single-stage models, YOLO11-SEG and YOLO26-SEG, for joint object detection and instance segmentation. A dedicated dataset of 10,542 annotated images with 23,420 labeled instances was constructed, covering lane markings, directional arrows, and pedestrian crossings. Both models were trained under identical conditions using a unified multi-task loss combining IoU-based regression, objectness, classification, and hybrid Binary Cross-Entropy and Dice segmentation losses. Performance was evaluated using precision, recall, F1-score, mAP@0.5, mAP@0.5-0.95, FPS, and FPS/W. Deployment was conducted on NVIDIA Jetson Nano, Raspberry Pi 5, Raspberry Pi 5 with Intel Movidius VPU, and Raspberry Pi 5 with Hailo-8 NPU. Results: Both models achieved high detection and segmentation performance, with mAP@0.5 exceeding 98%. YOLO26-SEG demonstrated superior inference speed and energy efficiency across all platforms, achieving higher FPS and FPS/W than YOLO11-SEG. The Hailo-8 NPU configuration achieved the best embedded performance, reaching 50-52.6 FPS and 18.85 FPS/W for YOLO26-SEG. Conclusion: EdgeLane-SEG effectively balances accuracy, efficiency, and deployment feasibility. YOLO26-SEG with Hailo-8 NPU acceleration is particularly suitable for real-time embedded ADAS applications, enabling energy-efficient and reliable road perception in resource-constrained environments.