Detection of Insulator Self-Blast with Improved YOLOV4 and Dilated Convolution
Zeli Wang, Hongjun Huang, Wentao Zhou, Chang Liu, Fanchen Meng, Guangtao Wei · 2023
Due to prolonged high-load operation and the impact of external environmental factors, insulators often undergo selfblasting, leading to significant disruptions in the functioning of the power system. This research introduces a novel approach for detecting insulator self-blasting incidents based on the YOLOv4 model. This method combines several advanced techniques, including feature fusion, attention mechanisms, and dilated convolution, to achieve highly accurate detection results. In the proposed method, shallow-level information is utilized to compensate for the loss of intricate details in deep-level features. Additionally, an attention mechanism is employed to enhance the precision of target positioning. Furthermore, dilated convolution is integrated to capture vital target context information and expand the network's receptive field. The experimental results demonstrate a remarkable improvement in the average detection precision of the enhanced model, showcasing an impressive increase of 6.86% compared to the baseline YOLOv4 model. This advancement promises to contribute significantly to the early identification and mitigation of insulator self-blasting issues, thereby enhancing the overall reliability and performance of the power system.