Combined Use of 3D and HSI for the Classification of Printed Circuit Board Components

Abstract

Successful recycling of electronic waste requires accurate separation of materials such as plastics, PCBs and electronic components on PCBs (capacitors, transistors, etc.). This article therefore proposes a vision approach based on a combination of 3D and HSI data, relying on the mutual support of the datasets to compensate existing weaknesses when using single 3D- and HSI-Sensors. The combined dataset serves as a basis for the extraction of geometric and spectral features. The classification is performed and evaluated based on these extracted features which are exploited through rules. The efficiency of the proposed approach is demonstrated using real electronic waste and leads to convincing results with an overall accuracy (OA) of 98.24%. To illustrate that the addition of 3D data has added value, a comparison is also performed with an SVM classification based only on hyperspectral data.

PCB-Vision: A Multiscene RGB-Hyperspectral Benchmark Dataset of Printed Circuit Boards

“…Sudharshan et al [31] proposed an enhanced version of the FasterRCNN object detection method called GOL-based Faster-RCNN that utilizes RGB and FX17 HSI camera information for enhancing the object detection performance of PCB components for aiding the recycling and recovery systems of PCB. Polat et al [32] fused 3D point clouds and HSI for classifying different types of objects and materials of electronic waste like shredded PCBs and plastics, this usage allowed the combination of geometrical and physical information to help components distinguish routines of PCB components.…”

Research on Detection Technology of Micro- Components on Circuit Board Based on Digital Image Processing

“…Polar et al focused on the classification and recycling of electronic waste, using the combined 3D and HIS data to construct a data set of capacitors, transistors, and other components to extract geometric and spectral features for the classification, recycling, and inspection of electronic components. Huang et al selected 4 kinds of resistors and capacitors to construct 1026 data sets, established the YOLOv2 detection model, and completed the identification and localization of electronic components [14,15] . This design is to realize image registration and chip identification of tiny components of circuit boards based on the powerful computing power of MATLAB.…”