A Low-Complexity YOLOv8s-Based Model for Environment Perception in Power Maintenance Guided Vehicles

Traditional methods for moving power covers in the power maintenance sector introduce complexities and safety risks due to their heavy reliance on manual labor. In order to reduce dependence on human resources while enhancing safety in operational processes, we introduce a low-complexity model called YOLO-EPGV (Environmental Perception in Guided Vehicles YOLO), which is built upon YOLOv8s to address this challenge. This model is specifically tailored for environmental perception in engineering vehicles used for power maintenance, aiming to advance vehicle automation, and minimize labor requirements and accident risks. YOLO-EPGV combines the StartNet architecture, Partial Convolution (PConv), and Convolutional Gated Linear Units (CGLU) to improve detection precision (mAP@50) while reduce computational costs. Furthermore, we developed a new greenbelt dataset, which was used for training and evaluating the model. The results show that YOLO-EPGV delivers excellent performance, achieving a mAP@50 of 92.8% with minimal computational load, outperforming several YOLO variants. Ablation studies confirm the contribution of each component to the overall performance, and comparisons with other lightweight backbone networks highlight the superior efficiency and precision of StartNet. Subsequently, the model was deployed on the NVIDIA Jetson Orin Nano, achieving a detection speed of 57 FPS while maintaining mAP@50 of 92.7%. This achievement has been validated through empirical testing in real-world operational settings.