Reconstruction Error Guided Instance Segmentation for Infrared Inspection of Power Distribution Equipment.

The instance segmentation of power distribution equipment in infrared images is a prerequisite for determining its overheating fault, which is crucial for urban power grids. Due to the specific characteristics of power distribution equipment, the objects are generally characterized by small scale and complex structure. Existing methods typically use a backbone network to extract features from infrared images. However, the inherent down-sampling operations lead to information loss of objects. The content regions of small-scale objects are compressed, and the edge regions of complex-structured objects are fragmented. In this paper, (1) the first unmanned aerial vehicle-based infrared dataset PDI for power distribution inspection is constructed with 16,596 images, 126,570 instances, and 7 categories of power equipment. It has the advantages of large data volume, rich geographic scenarios, and diverse object patterns, as well as challenges of distribution imbalance, category imbalance, and scale imbalance of objects. (2) A reconstruction error (RE)-guided instance segmentation framework, coupled with an object reconstruction decoder (ORD) and a difference feature enhancement (DFE) module, is proposed. The former reconstructs the objects, where the reconstruction result indicates the position and degree of information loss of the objects. Therefore, the difference map between the reconstruction result and the input image effectively replays the object features. The latter adaptively compensates for object features by global fusion between the difference features and backbone features, thereby enhancing the spatial representation of objects. Extensive experiments on the constructed and publicly available datasets demonstrate the strong generalization, superiority, and versatility of the proposed framework.