Printed circuit board identification using deep convolutional neural networks to facilitate recycling

In this paper, we have proposed a robust Printed Circuit Board (PCB) classification system based on computer vision and deep learning to assist sorting e-waste for recycling. We have used a public PCB dataset acquired using a conveyor belt, as well as a locally developed PCB dataset that represents challenging practical conditions such as varying lighting, orientation, distance from camera, cast shadows, view-points and different cameras/resolutions. A pre-trained EfficientNet-B3 deep learning model is utilized and retrained for use with our data in PCB classification context. Deep nets are designed for closed set recognition tasks capable of classifying only the images they have been trained for. We have extended the closed set nature of deep nets for use in our open set classification tasks which require identifying unknown PCBs apart from classifying known PCBs. We have achieved an open set average accuracy of 92.4% which is state of the art given the complexities in the datasets we worked with.