Terminal strip detection and recognition based on improved YOLOv7-tiny and MAH-CRNN+CTC models

Abstract

For substation secondary circuit terminal strip wiring, low efficiency, less easy fault detection and inspection, and a variety of other issues, this study proposes a text detection and identification model based on improved YOLOv7-tiny and MAH-CRNN+CTC terminal lines. First, the YOLOv7-tiny target detection model is improved by the introduction of the spatially invariant multi-attention mechanism (SimAM) and the weighted bidirectional feature pyramid network (BiFPN). This also improves the feature enhancements and feature fusion ability of the model, balances various scales of characteristic information, and increases the positioning accuracy of the text test box. Then, a multi-head attention hybrid (MAH) mechanism is implemented to optimize the convolutional recurrent neural network with connectionist temporal classification (CRNN+CTC) so that the model could learn data features with larger weights and increase the recognition accuracy of the model. The findings indicate that the enhanced YOLOv7-tiny model achieves 97.39%, 98.62%, and 95.07% of precision, recall, and mean average precision (mAP), respectively, on the detection dataset. The improved MAH-CRNN+CTC model achieves 91.2% character recognition accuracy in the recognition dataset.