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Faulty RJ45 connectors detection on radio base station using deep learning

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Abstract

A Radio Base Station (RBS), part of the Radio Access Network, is a particular type of equipment that supports the connection between a wide range of cellular user devices and an operator network access infrastructure. Nowadays, most of the RBS maintenance is carried out manually, resulting in a time consuming and costly task. A suitable candidate for RBS maintenance automation is repairing faulty links between devices caused by missing or unplugged connectors. This paper proposes and compares two Deep Learning (DL) solutions applied to identify attached RJ45 connectors on network ports. We named Connector Detection, the DL solution based on object detection, and Connector Classification, the one based on object classification. With connector detection, we achieve an accuracy of 0.934 and a mean average precision of 0.903. Connector Classification, reaches a higher maximum Accuracy of 0.981 and an Area Under the Receiving Operating characteristic Curve (AUC) of 0.989. Although Connector Detection was outperformed in this particular study, it is more flexible for scenarios where there is a lack of precise information about the environment and the possible devices. This in contrast with Connector Classification which requires such information to be well-defined beforehand.

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Authors and Affiliations

  1. Informatics Center, Federal University of Pernambuco, Recife, Brazil

    Marrone Silvério Melo Dantas, Pedro Henrique Dreyer Leuchtenberg, Gabriel Fonseca Reis de Souza, Daniel Bezerra, Judith Kelner & Djamel Fawzi Hadj Sadok

  2. Ericsson Research, Indaiatuba, Brazil

    Ricardo Souza & Silvia Lins

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  1. Marrone Silvério Melo Dantas
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Dantas, M.S.M., Leuchtenberg, P.H.D., de Souza, G.F.R. et al. Faulty RJ45 connectors detection on radio base station using deep learning. Multimed Tools Appl 81, 30305–30327 (2022). https://doi.org/10.1007/s11042-022-12694-6

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