Lightweight Dual-YOLOv8 Instance-Aware Semantic Segmentation for Real-Time Autonomous Driving on Edge ARM/GPU Platforms

Semantic segmentation is a fundamental component of autonomous driving systems, enabling accurate scene understanding and object-level perception. However, achieving precise instance-level delineation while maintaining real-time performance on resource-constrained platforms remains a significant challenge, particularly for edge deployment scenarios. This paper proposes a lightweight dual-YOLOv8 fusion framework for instance-aware semantic segmentation in autonomous driving applications. The proposed approach integrates YOLOv8n-seg and YOLOv8s-seg through a multi-scale fusion strategy that exploits their complementary feature representations to improve the segmentation of road-relevant objects, including cars, buses, trucks, and motorcycles. The framework is evaluated on the Reetiquetado de Vehiculos dataset using standard instance-level segmentation metrics. Experimental results demonstrate strong performance, achieving an overall mAP@0.5 of 92.9% and mAP@0.5:0.95 of 80.8%, while maintaining real-time inference with an average processing time of 7.9 ms per image (126 FPS) on an NVIDIA RTX 3050 GPU. Class-wise and confidence-based analyses confirm consistent segmentation accuracy across vehicle categories, highlighting the robustness of the proposed fusion strategy in handling scale variation, occlusions, and object diversity. In addition, an embedded deployment analysis provides insight into the feasibility and practical constraints of deploying the proposed framework on representative edge platforms. Overall, the proposed dual-YOLOv8 fusion framework achieves an effective balance between segmentation accuracy and computational efficiency, making it suitable for real-time autonomous driving perception on edge ARM/GPU platforms and Advanced Driver Assistance Systems (ADAS).