Work

Lightweight Insulator Defect Detection in High-Resolution UAV Imagery via System-Level Co-Design

Year: 2026

Type: article

Abstract: The inspection of minuscule insulator defects from high-resolution (HR) UAV imagery presents a significant algorithmic challenge. The severe scale mismatch between HR images and low-resolution model inputs often leads to feature distortion for sparsely distributed targets. To address these issues, this paper proposes an integrated data–model collaborative framework. At the data level, an offline label-guided optimal tiling (LGOT) strategy is introduced to alleviate scale mismatch by curating information-dense training tiles. At the model level, we design the semi-decoupled prior-driven detection head (SDPD-Head), which leverages evolutionary priors to stabilize the learning of microscopic spatial features. During inference, an online inference-time adaptive tiling (ITAT) strategy is used to match the spatial scale distribution between training and inference and to reduce feature loss caused by direct downscaling. Experiments on a real-world inspection dataset show that the proposed framework achieves an mAP@50 of 92.9% with 2.17 M parameters and 4.7 GFLOPs.

Source: Remote Sensing

DOI: 10.3390/rs18060953

Authors: Yujie Zhu, Guanhua Chen, Linghao Zhang, Jiajun Zhou, Junwei Kuang, Jiangxiong Zhu