Abstract
Fiber transfer box is a box containing fiber ports which transfer optical signals for telecommunication networks. It’s hard to check and record the status of hundreds of ports using man power. This paper proposes an intelligent recognition and localization system for fiber transfer box. Pictures of ports are treated and classified with Support Vector Machine (SVM) and Deep learning algorithm. It can identify the status of all ports in the fiber transfer box and recognize the writing letter for each layer. First, the image is converted to binary one by threshold of the Cr channel. A SVM classifier is used to identify the Red-Hat ports by HOG features. Second, an adaptive chessboard segmentation algorithm is designed for segmentation of all ports and character area. In order to improve the identification accuracy, an eleven-layer Convolution Neural Network (CNN) is trained and used to further identify the ports which have not been classified correctly by SVM. Letters are extracted and positioned by using YOLOv3-tiny network. Experiments show that the method achieves great accuracy and efficiency for a variety of scenes on mobile devices.
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Li, M., Ao, Y., Peng, W. et al. Research of status recognition of Fiber transfer box based on machine vision and deep learning. Multimed Tools Appl 79, 28695–28709 (2020). https://doi.org/10.1007/s11042-020-09327-1
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DOI: https://doi.org/10.1007/s11042-020-09327-1