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.
Sensors (Basel). 2020 Oct 12;20(20):5762. doi: 10.3390/s20205762.
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.
输送带是采矿领域大量物料最广泛的运输方式。因此，能够帮助人类执行输送带系统检查的自主方法是公司关注的主要问题。在这种情况下，我们在这项工作中提出了一种新颖的自动视觉探测器，可以识别输送带结构上的污垢堆积，这是维护检查员的任务之一。这种视觉探测器可以作为传感器嵌入自主机器人中进行检查活动。所提出的系统涉及从 RGB 图像训练卷积神经网络。使用迁移学习技术，即使用我们收集的图像重新训练用于图像分类的成熟网络，已证明非常有效。分析了两种不同的迁移学习方法。表现最好的方法在污垢识别方面的平均准确率为 0.8975，F1 得分为 0.8773。现场验证实验用于评估所提出的系统在实时分类任务中的性能。
