Yuhang Cai

Aiming at the core pain point that in existing object detection models, it is difficult to balance detection accuracy and real-time inference efficiency on edge computing devices in UAV-based intelligent inspection of power transmission lines, this paper proposes a lightweight YOLO-PowerLiteV2 model for anomaly target detection in power transmission lines to address the shortcomings of YOLO-PowerLite. Based on YOLO11n as the baseline, the model achieves compression of model volume while guaranteeing detection performance through four core improvements: the C3k2-UIB lightweight backbone module, the MCA (Multi-scale Cross-Axis) attention mechanism, the MBConv lightweight detection head, and the MFM (Modulation Feature Fusion) module. Experiments were conducted on a dataset constructed from 5563 aerial images of transmission lines containing three types of targets: bird nests, defective insulators, and balloons. The results show that YOLO-PowerLiteV2 achieves a mAP@50 of 95.2%, with only 0.97 M parameters and 2.8 G floating point operations (FLOPs). Compared with the baseline model, the number of parameters is reduced by 62.5%, and FLOPs are decreased by 56.25%. On the NVIDIA Jetson Xavier NX edge platform, the model achieves 59.5 FPS with only 16.8 ms latency, outperforming the baseline by 31% in frame rate. Its comprehensive performance outperforms mainstream lightweight detection models. The model demonstrates excellent adaptability to UAV edge-terminal deployment requirements, thereby providing technical support for real-time intelligent inspection of power transmission lines.

Transmission lines traversing forested areas pose significant fire risks, necessitating timely and efficient inspection mechanisms. Traditional manual patrols and cloud-based UAV inspections suffer from high latency, bandwidth dependence, and delayed response times. To address these challenges, this study proposes an integrated, real-time UAV-edge computing system for the early identification of fire risks and structural hazards along transmission corridors. The system integrates a DJI M300 RTK UAV with a Manifold 2-G edge computing unit (based on NVIDIA Jetson TX2), deploying a lightweight, TensorRT-optimized YOLOv8 model. By leveraging FP16 precision quantization and operator fusion, the system achieves a real-time inference speed of 32 FPS on the embedded platform. Furthermore, a custom Payload SDK integration ensures automated image acquisition and closed-loop data transmission via a dual-mode (4G/5G + Wi-Fi) communication link. Field experiments demonstrate that the system significantly reduces data transmission latency while maintaining high detection accuracy (mAP > 94%), providing a robust and replicable solution for intelligent power grid maintenance in resource-constrained environments.