PCBSegClassNet — A light-weight network for segmentation and classification of PCB component

PCB component classification and segmentation can be helpful for PCB waste recycling. However, the variance in shapes and sizes of PCB components presents crucial challenges. We propose PCBSegClassNet, a novel deep neural network for PCB component classification and segmentation. The network uses a two-branch design that captures the global context in one branch and spatial features in the other. The fusion of two branches allows the effective segmentation of components of various sizes and shapes. We reinterpret the skip connections as a learning module to learn features efficiently. We propose a texture enhancement module that utilizes texture information and spatial features to obtain precise boundaries of components. We introduce a loss function that combines DICE, IoU, and SSIM loss functions to guide the training process for precise pixel-level, patch-level, and map-level segmentation. Our network outperforms all previous state-of-the-art networks on both segmentation and classification tasks. For example, it achieves a DICE score of 96.3% and IoU score of 92.7% on the FPIC dataset. From the FPIC dataset, we crop the images of 25 component classes and term the resultant 19158 images as the “FPIC-Component dataset” (we release scripts for obtaining this dataset from FPIC dataset). On this dataset, our network achieves a classification accuracy of 95.2%. Our model is much more light-weight than previous networks and achieves a segmentation throughput of 122 frame-per-second on a single GPU. We also showcase its ability to count the number of each component on a PCB. The code is available at https://github.com/CandleLabAI/PCBSegClassNet.