Robust Lightweight UAV Inspection System for Consumer Electronics Applications in Smart Grids

With the expansion of smart grids, and the increasing need for efficient infrastructure management, UAVs have become vital for tasks such as object detection and smart grid inspections. However, current UAV-based systems face challenges related to environmental factors (e.g., fog, wind, and low visibility) and limited computational power, which restrict the use of advanced computer vision techniques. This work presents a lightweight and robust YOLOv8 (LR-YOLOv8) model designed for consumer electronics applications, bridging the gap between advanced computer vision algorithms and real-time operations on resource-constrained UAVs. The model integrates a lightweight architecture, a soft thresholding strategy to handle noisy data, and hue, saturation, and value (HSV) augmentation to simulate complex environments. Additionally, a multiscale domain-adapted loss function improves the mean average precision (mAP) and robustness. The experimental results demonstrate that, compared with the existing object detection methods, LR-YOLOv8 reduces the model weight, and increases the number of frames per second (FPS), while achieving a higher mAP.