Deep learning-based inclined power signboard identification method and system

The multi-directional multi-scale Log-Gabor filter and the adaptive scale fusion module generate a binary segmentation mask, combined with polar coordinate transformation and lightweight CRNN, solve the problems of low recognition accuracy and high computational complexity of tilted power signs in complex environments, and achieves an efficient and robust recognition effect.

CN120451177AActive Publication Date: 2025-08-08STATE GRID HUNAN ELECTRIC POWER COMPANY LIMITED +2
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Patent Information

Application Number
CN202510965739.4
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-07-14
Publication Date
2025-08-08
Estimated Expiration
2045-07-14

AI Technical Summary

Technical Problem

The prior art is difficult to effectively identify power signs with incline, rotation and light variation in complex environments, especially in multi-scale and occlusion situations, with low recognition accuracy and high computational complexity, making it difficult to meet the real-time deployment requirements.

Method used

A multi-directional multi-scale Log-Gabor filter is used to extract key points, combine the adaptive scale fusion module and the differentiable binarization module to generate a binarized segmentation mask, tilt correction and character prediction are performed through polar coordinate transformation and rotation invariance feature descriptors, and character recognition is performed with lightweight CRNN.

Benefits of technology

It realizes efficient and robust tilted power sign recognition in complex scenarios, improves identification accuracy and computing efficiency, and meets the needs of real-time deployment.

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Abstract

The invention provides an inclined electric power signboard identification method and system based on deep learning. The method comprises the following steps: taking a to-be-detected electric power signboard image as an input image; carrying out multi-direction and multi-scale filtering to extract key points so as to generate a space mask; extracting multi-scale features from an input image, dynamically weighting and fusing the multi-scale features and associating a spatial mask, and generating a binary segmentation mask; calculating a mass center position of the binarized segmentation mask as an original point position of polar coordinates, performing polar coordinate transformation on the input image, performing multi-direction multi-scale filtering on the obtained polar coordinate domain image, calculating a multi-scale feature descriptor, and further obtaining an RMLG descriptor; and carrying out tilt correction on the input image based on the centroid position and the multi-scale feature descriptor, and carrying out character prediction on the image subjected to tilt correction in combination with the RMLG descriptor to obtain a character prediction sequence corresponding to the input image. According to the invention, identification of the inclined power equipment signboard in a complex scene can be realized.
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