Automatic System for Visual Detection of Dirt Buildup on Conveyor Belts Using Convolutional Neural Networks

Sensors

AgnaldoJ. da R. Reis¹  FredericoG. Guimarães²  AndréA. Santos³  FilipeA. S. Rocha⁴ 

[1] Department of Control Engineering and Automation, School of Mines, Federal University of Ouro Preto (UFOP), Minas Gerais 35000-400, Brazil;Department of Electrical Engineering, Federal University of Minas Gerais (UFMG), Minas Gerais 31270-901, Brazil;Programa de Pós-Graduação em Instrumentação, Controle e Automação de Processos de Mineração, Universidade Federal de Ouro Preto e Instituto Tecnológico Vale, Minas Gerais 35400-000, Brazil;Robotics Lab, Vale Institute of Technology (ITV), Minas Gerais 35400-000, Brazil;

关键词: convolutional neural network;    conveyor belt;    machine learning;   

DOI  :  10.3390/s20205762

来源: DOAJ

【 摘 要 】

Conveyor belts are the most widespread means of transportation for large quantities of materials in the mining sector. Therefore, autonomous methods that can help human beings to perform the inspection of the belt conveyor system is a major concern for companies. In this context, we present in this work a novel and automatic visual detector that recognizes dirt buildup on the structures of conveyor belts, which is one of the tasks of the maintenance inspectors. This visual detector can be embedded as sensors in autonomous robots for the inspection activity. The proposed system involves training a convolutional neural network from RGB images. The use of the transfer learning technique, i.e., retraining consolidated networks for image classification with our collected images has shown very effective. Two different approaches for transfer learning have been analyzed. The best one presented an average accuracy of 0.8975 with an F-1 Score of 0.8773 for the dirt recognition. A field validation experiment served to evaluate the performance of the proposed system in a real time classification task.

【 授权许可】

Unknown