Panel Lights Detection Based Automatically Equipment Monitoring Through Deep Learning Method

Abstract

Automatically equipment monitoring is important in automation field. It asks for the combination of image processing, pattern recognition and the corresponding computer vision technology. Since most equipment status could be read through the panel lights, this paper proposed a deep learning method to automatically monitor devices through detecting their signal lights from panel images captured by digital camera. The entire method employed Unet model to be the backbone model. The feature maps of different scales extracted by the encoder of Unet were weighted fused together to generate the final output of the detected lights. The model were trained on our equipment panel image set. The validation showed that the proposed method could detect the small panel lights accurately.