Automatic State Recognition of Multi-type Protection Platens based on YOLOv5 and Parallel Multi-Decision Algorithm

A protection platen is a vital component in relay protection systems. The manual inspection of protection platen states is long-term repetitive work with low efficiency and imposes a heavy burden on workers. In this work, we propose a new system to automatically detect the states of multi-type protection platens in images. This system can classify two protection platen categories and further recognize the states of protection platens. For the classification of protection platen types, we propose a new algorithm that automatically detects two protection platen types based on HSV (Hue, Saturation, Value) color space weighting operators. The proposed operators quantify the color variation in the protection platen and reduce the influence of environmental factors. With respect to the state recognition of protection platens, the Type-I protection platen states are automatically classified by the YOLOv5 (You only look once version 5) network. Since the Type-II protection platen has three primary states and more complicated structures, we investigate a new parallel multi-decision algorithm to recognize the states of Type-II protection platens based on the newly proposed watershed-color space difference-shape feature (W-CD-SF) method and the YOLOv5 network. The W-CD-SF technique can segment the protection platens and extract their shape features automatically. This multi-decision mechanism improves the robustness and generalization of state recognition. Experiments were conducted on the collected protection platen images containing 8,969 protection platens. The recognition accuracies of protection platen states exceed 95%. This system can provide auxiliary detection and long-term monitoring of protection platen states.

@article{Zhang2022,
  title     = {Automatic State Recognition of Multi-type Protection Platens based on YOLOv5 and Parallel Multi-Decision Algorithm},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {13},
  number    = {7},
  year      = {2022},
  publisher = {The Science and Information Organization},
  author    = {Ying Zhang and Yihui Zhang and Hao Wu and Boxiong Fu and Ling He},
  doi       = {10.14569/IJACSA.2022.0130736},
  url       = {https://doi.org/10.14569/IJACSA.2022.0130736}
}