Dilated Involutional Pyramid Network (DInPNet): A Novel Model for Printed Circuit Board (PCB) Components Classification

Ananya Mantravadi1, Dhruv Makwana2, Sai Chandra Teja R2, Sparsh Mittal3, Rekha Singhal4
1IIIT Raichur, 2Independent Researcher, 3IIT Roorkee, 4TCS Research


The rapid growth in the volume and complexity of PCB design has encouraged researchers to explore automatic visual inspection of PCB components. Automatic identification of PCB components such as resistors, transistors, etc., can provide several benefits, such as producing a bill of materials, defect detection, and e-waste recycling. Yet, visual identification of PCB components is challenging since PCB components have different shapes, sizes, and colors depending on the material used and the functionality.

The paper proposes a lightweight and novel neural network, Dilated Involutional Pyramid Network (DInPNet), for the classification of PCB components on the FICS-PCB dataset. DInPNet makes use of involutions superseding convolutions that possess inverse characteristics of convolutions that are location-specific and channel-agnostic. We introduce the dilated involutional pyramid (DInP) block, which consists of an involution for transforming the input feature map into a low-dimensional space for reduced computational cost, followed by a pairwise pyramidal fusion of dilated involutions that resample back the feature map. This enables learning representations for a large effective receptive field while at the same time bringing down the number of parameters considerably. DInPNet with a total of 531,485 parameters achieves 95.48\% precision, 95.65\% recall, and 92.59\% MCC (Matthew's correlation coefficient). To our knowledge, we are the first to use involution for performing PCB components classification.