Abstract
With the rising global demand for electricity, transmission infrastructure is becoming increasingly important as a key support for ensuring stable and reliable power supply.In recent years, UAVs have been widely used in the inspection and maintenance of transmission equipment due to their advantages of high efficiency, flexibility and intelligence, which have greatly improved the operation and maintenance efficiency and safety level.However, the transmission equipment itself is exposed to harsh natural environments during prolonged use, such as high temperatures, humidity changes, wind and sand erosion, as well as electromagnetic interference, coupled with complex topographical features, such as mountainous, hilly, and forested areas, which result in the transmission equipment inspection process being challenged by occlusion and large differences in dimensions.To cope with these problems, this paper proposes ACCYolo. a model based on the YOLOv10n architecture with the goal of improving image detection of transmission equipment under multi-scale and occluded targets in UAV-based scenes.On the one hand, the ACCYolo model, to solve the occlusion problem, incorporates the Acmix model, which incorporates the self-attention mechanism to achieve dynamic feature extraction, effectively improving the detection performance of the model in overlapping scenes.On the other hand, in order to cope with the size difference problem in multi-scale detection, the GELAN structure combines a lightweight design with the Programmable Gradient Information (PGI) mechanism to improve the accuracy of multi-scale target detection, while the ASFF module is designed to improve the accuracy of multi-scale target detection through adaptive spatial feature fusion.The experimental results show that. The proposed method shows significant advantages in transmission equipment monitoring tasks, Overall mAP@50 raise to 0.950, and provides an effective program to ensure the reliability of power supply.