Dual-Path Enhanced YOLO11 for Lightweight Instance Segmentation with Attention and Efficient Convolution

Abstract

Instance segmentation stands as a foundational technology in real-world applications such as autonomous driving, where the inherent trade-off between accuracy and computational efficiency remains a key barrier to practical deployment. To tackle this challenge, we propose a dual-path enhanced framework based on YOLO11l. In this framework, two improved models, YOLO-SA and YOLO-SD, are developed to enable high-performance lightweight instance segmentation. The core innovation lies in balancing precision and efficiency through targeted architectural advancements. For YOLO-SA, we embed the parameter-free SimAM attention mechanism into the C3k2 module, yielding a novel C3k2SA structure. This design leverages neural inhibition principles to dynamically enhance focus on critical regions (e.g., object contours and semantic key points) without adding to model complexity. For YOLO-SD, we replace standard backbone convolutions with lightweight SPD-Conv layers (featuring spatial awareness) and adopt DySample in place of nearest-neighbor interpolation in the upsampling path. This dual modification minimizes information loss during feature propagation while accelerating feature extraction, directly optimizing computational efficiency. Experimental validation on the Cityscapes dataset demonstrates the effectiveness of our approach: YOLO-SA increases mAP from 0.401 to 0.410 with negligible overhead; YOLO-SD achieves a slight mAP improvement over the baseline while reducing parameters by approximately 5.7% and computational cost by 1.06%. These results confirm that our dual-path enhancements effectively reconcile accuracy and efficiency, offering a practical, lightweight solution tailored for resource-constrained real-world scenarios.