An efficient multi-order cascade distillation model for the detection of small targets and occluded objects in transmission line inspection

Abstract

The cascade distillation detection model is proposed to address the problems of low accuracy of multiple small target defect detection and lack of robustness under complex environments in transmission line UAV intelligent inspection. Firstly, the cascading modules of the shared backbone network are designed: the dynamic anchor box generation module and the bounding box refinement module. The former collects and processes areas where there may be targets, outputs high-quality location information, and distills it to the latter, and the latter is responsible for refining and filtering this part of the information, which solves the problems of missed detection of model targets and lack of a priori information on target locations. Secondly, a high-order aggregated feature extraction backbone network is proposed to enhance the feature extraction of small and medium-sized targets and obscured targets on transmission lines. Then, a spatially deformable attention mechanism is designed to optimize the selection of sampling points for transmission line targets and defects and the calculation of their attention weights by aggregating contextual information in the vicinity of the targets, enhancing the module's ability to refine and adjust the bounding boxes. Finally, a large dataset of transmission line defect targets is constructed and generated, and the validity of the model is verified by experiments.