Edge-Optimized Semi-Supervised Deep Learning for Power Line Component Inspection

Power line component inspection is essential for maintaining the reliability of the electrical power infrastructure. Recently, some researchers have studied automatic power line inspection using drones and deep learning. However, fully supervised deep learning approaches require large amounts of labeled data that are difficult and expensive to obtain in real-world environments. To address these challenges, this paper proposes an edge-optimized semi-supervised deep learning framework for power line component inspection. The proposed approach combines a semi-supervised learning (SSL) strategy to leverage both limited labeled images and abundant unlabeled field data with hardware-software (HW-SW) co-optimization techniques for efficient deployment on resource-constrained edge devices. In the learning stage, the framework improves detection performance by leveraging unlabeled inspection data via pseudo-labeling and confidence-based sample selection, thereby reducing annotation effort while maintaining robust recognition performance. In the deployment stage, the quantization technique was applied to enable real-time operation on embedded platforms with limited computational resources and power budgets. In this paper, an improved version of the edge-AI deployment score, the generalized edge-AI deployment score (GEADS), is proposed. In SSL evaluation, debiased semi-supervised learning (DeSSL) achieves a higher observed mAP@0.5 and F1-score than the standard SSL method in the single-run simulations using dataset 1 and dataset 2. In hardware evaluation, the YOLOv7-Tiny (INT8) configuration implemented on a Raspberry Pi 5 achieves the highest GEADS of 0.657, confirming it offers the most balanced performance among the required parameters. From the simulation, it is also confirmed that the proposed GEADS provides a more interpretable and statistically stable metric than the existing metric to evaluate the edge deployment.