YOLO‐GDCNN: Real‐Time Operating Point Detection for Live Working Robots in the Power Industry

ABSTRACT In the power industry maintenance, the capability of live working robots to detect and operate with power components in real time is paramount. This paper proposes a cascaded detection framework for real‐time detection of live working operation points, named YOLO‐GDCNN. The framework consists of two parts. The first part is the proposed Lightweight YOLOv5 (Li‐YOLOv5), which is composed of Stem, ShuffleBlock and SimSPPF, enhancing the network's generalisation ability while reducing the computational load. The second part proposes a Generative Dense Convolutional Neural Network (GDCNN), which improves the detection accuracy of the lightweight network through dense modules. Through the series of the above modules, the framework successfully combines detection accuracy and real‐time performance. Finally, we design experiments in simulated laboratory environments and real‐world live working scenarios. The experimental results demonstrate that the proposed YOLO‐GDCNN is adept at real‐time and accurate detection of live working object types and their operating point, demonstrating its suitability for live working robots.