Balancing Accuracy and Efficiency With a Multiscale Uncertainty-Aware Knowledge-Based Network for Transmission Line Inspection

Real-world transmission line inspections (RTLIs) ensure power stability and safety. Deep learning (DL) models have become prevalent approaches for performing RTLI tasks. However, the high computational demands and substantial parameter requirements of DL models limit their real-world applicability. This article introduces a novel approach, a multiscale uncertainty-aware knowledge-based network, which is designed to balance the accuracy and efficiency in RTLI tasks. Specifically, we propose an uncertainty-aware knowledge distillation method that incorporates pixel-level uncertainty into the knowledge transfer process, mitigating the impact of noisy knowledge derived from extra background information contained in ground truths. In addition, our method integrates a multiscale relationship distillation technique, thus enhancing the transfer of multiscale information between the teacher and student models. Consequently, RTLI tasks can be efficiently accomplished using the well-learned lightweight student model. Comprehensive experiments conducted on a real-world dataset collected via uncrewedaerial vehicles demonstrate the efficacy of our proposed approach in terms of achieving high detection accuracy with reduced computational costs.