Tonghui He and Jimin LaiThis email address is being protected from spambots. You need JavaScript enabled to view it.
Hebei Software Institute, Baoding, Hebei 071000, China
Received: January 31, 2024 Accepted: February 29, 2024 Publication Date: May 7, 2024
Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.
Currently, the visual-based switch status detection technology of cabinet panels faces many challenges, such as the inaccurate switch status matching caused by the variable shooting Angle and uncontrollable illumination change. To address these issues, this paper proposes a cabinet panel switch state recognition method based on an improved convolutional neural network. The proposed method uses the improved convolutional neural network to extract the features of the input image, then nominate the target region and submit the nomination region to the RPN network for target detection and position regression. Experiments on the open dataset Switch status-1501 and the self-built workshop dataset verify the effectiveness of the proposed method.
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