YOLO26-Based Multi-Resolution Adaptive Insulator Defect Detection on Ascend NPU Edge Devices

The You Only Look Once (YOLO) series has consistently advanced the field of object detection, evolving from YOLOv1 to the latest YOLO26, achieving remarkable improvements in detection accuracy and computational efficiency. However, deploying such high-performance models on resource-constrained edge devices remains challenging, particularly for tasks requiring real-time inference. A critical yet often overlooked factor affecting edge deployment is the trade-off between input image resolution and computational cost: while higher resolution preserves fine-grained details essential for detecting small defects, it proportionally increases energy consumption and latency. To address this issue, we propose a novel multi-resolution adaptive detection framework based on YOLO26, specifically optimized for Ascend NPU edge devices. Our method dynamically selects the most suitable input resolution for each inference instance via a jointly optimized scene complexity metric, where the feature weights and resolution thresholds are simultaneously calibrated through Bayesian multi-objective optimization to achieve an optimal balance between predictive accuracy and energy efficiency. The experiments on transmission line insulator defect detection demonstrate that our approach achieves favorable trade-offs, maintaining high detection precision while significantly reducing power consumption compared to fixed-resolution baselines. The proposed framework provides a viable solution for intelligent visual inspection in power grid infrastructure.