Abstract
Amidst the ongoing advancements in modern industrial automation and intelligent manufacturing, the inspection operations of underground substations have become a pivotal research domain. The traditional manual inspections, hampered by inefficiencies and potential misjudgements, are gradually falling short of contemporary demands. To address this, our research focuses on harnessing the object detection and classification capabilities of deep learning. Specifically, we centered our study on YOLOv8, analyzing and experimenting with its application in image recognition for underground substations. The experimental data suggests that YOLOv8 exhibits exceptional proficiency in tasks like target detection, image segmentation, and classification. For instance, it achieved commendable results in switch status detection, readings of pointer instruments, and binary classification of knife switch statuses. Overall, deep learning introduces an innovative, accurate, and efficient detection method for underground substations, laying a robust technological foundation for future intelligent manufacturing and automation.
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Hao, D., Jiang, W. (2024). Research and Application of Image Recognition Technology Based on YOLOv8 in Intelligent Inspection of Underground Substation. In: Dong, J., Zhang, L., Cheng, D. (eds) Proceedings of the 2nd International Conference on Internet of Things, Communication and Intelligent Technology. IoTCIT 2023. Lecture Notes in Electrical Engineering, vol 1197. Springer, Singapore. https://doi.org/10.1007/978-981-97-2757-5_59
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DOI: https://doi.org/10.1007/978-981-97-2757-5_59
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