Transmission lines are widely distributed in complex environments, making them susceptible to foreign object intrusion, which could lead to serious consequences, i.e., power outages. Currently, foreign object detection on transmission lines is primarily conducted through UAV-based field inspections. However, the captured data must be transmitted back to a central facility for analysis, resulting in low efficiency and the inability to perform real-time, industrial-grade detection. Although recent YOLO series models can be deployed on UAVs for object detection, these models’ substantial computational requirements often exceed the processing capabilities of UAV platforms, limiting their ability to perform real-time inference tasks. In this study, we propose a novel lightweight detection algorithm, SCL-YOLOv8, which is based on the original YOLO model. We introduce StarNet to replace the CSPDarknet53 backbone as the feature extraction network, thereby reducing computational complexity while maintaining high feature extraction efficiency. We design a lightweight module, CGLU-ConvFormer, which enhances multi-scale feature representation and local feature extraction by integrating convolutional operations with gating mechanisms. Furthermore, the detection head of the original YOLO model is improved by introducing shared convolutional layers and group normalization, which helps reduce redundant computations and enhances multi-scale feature fusion. Experimental results demonstrate that the proposed model not only improves the detection accuracy but also significantly reduces the number of model parameters. Specifically, SCL-YOLOv8 achieves a mAP@0.5 of 94.2% while reducing the number of parameters by 56.8%, FLOPS by 45.7%, and model size by 50% compared with YOLOv8n.

In recent years, the unmanned aerial vehicle-assisted internet of vehicles has been extensively studied to enhance communication and computation services in vehicular environments where ground infrastructures are limited or absent. However, due to the limited-service range and battery life of unmanned aerial vehicles, along with the high mobility of vehicles, an unmanned aerial vehicle cannot continuously cover and serve the same vehicle, leading to interruptions in vehicular application services. Therefore, this paper proposes a joint optimization strategy for task migration and power allocation based on soft actor-critic (JOTMAP-SAC). First, communication models, computational resource allocation models, and computation models are established sequentially based on the computational resource and dynamic coordinate of each node. The joint optimization problem of task migration and power allocation is then formulated. Considering the dynamic nature of the unmanned aerial vehicle-assisted internet of vehicles environment and the continuity of the action space, a soft actor-critic based algorithm for task migration and power allocation is designed. This algorithm iteratively finds the optimal solution to the joint optimization problem, thereby reducing the processing delay in unmanned aerial vehicle-assisted internet of vehicles and ensuring the continuity of internet of vehicles task processing.