Detect as You Fly: Using Attention-based YOLOv10 for Automatic Fault Detection of Transmission Line Insulators

Insulator fault detection constitutes a critical role in ensuring the reliability of power transmission lines. An innovative and efficacious inspection strategy involves combining the Unmanned Aerial Vehicles (UAVs) with deep learning models, notably the YOLO series, to automatically detect insulator status while in flight. To achieve this, building upon the YOLOv10 framework, this study integrates the Expectation Maximization Attention (EMA) module and the Space-to-Depth (SPD) layer to improve its network structure, thereby enhancing its ability to extract key information from input data and improving its robustness towards small objects and blurred images while ensuring the efficiency. Specifically, the EMA modules are introduced to connect each detection head module with the neck network to strengthen the model’s attention during the output process. Furthermore, the SPD layers are incorporated before several convolution modules to convert the spatial dimension of input into the depth dimension, thereby mitigating information loss and augmenting the model’s detection capability towards small and low-resolution objects. Subsequently, this attention-based model is verified based on a real-world dataset of insulator images. The results demonstrate its ability to improve recognition accuracy, achieving a mean average precision of 97.3%, while incurring only a 0.1 millisecond increase in processing time per image. This indicates that applying the attention-based model to UAV-based patrol strategies holds immense potential for high-precision and real-time fault detection, thus achieving automated fault detection process.