Evaluating Prompt Engineering for Generalized Power Line Segmentation

Power line segmentation is a critical task in infrastructure monitoring, UAV-based inspection, and autonomous navigation. Detecting power lines in aerial images presents significant challenges due to their thin structure, varying backgrounds, and occlusions caused by environmental elements. Recent advancements in multimodal large language models (MLLMs) and vision-based segmentation frameworks provide a new direction for improving segmentation accuracy using natural language instructions. This study investigates the effectiveness of prompt engineering in enhancing power line segmentation performance within the Generalized Segmentation Vision Assistant (GSVA) framework. By systematically evaluating different prompt strategies, we analyze how varying levels of prompt specificity influence the balance between precision, recall, and segmentation completeness. Our findings demonstrate that carefully structured prompts improve the model's ability to detect power lines while minimizing false positives from visually similar structures. Furthermore, we propose an optimized prompt that enhances segmentation performance by leveraging contrast-awareness, structural positioning, and adaptive instruction tuning. The results highlight the importance of prompt optimization in improving segmentation accuracy, particularly for thin-object detection in complex backgrounds.