# 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. hyperspectral imaging; 3D data; point cloud; classification; rule-based classification; waste sorting; WEEE; recycling +
Footnote †: journal: Applied Sciences  
1i3 mainz, Institute for Spatial Information and Surveying Technology, Mainz University of Applied Sciences, Luccy-Hillebrand-Str. 2, D-55128 Mainz, Germany; [EMAIL_ADDRESS] (S.P. ); frank.bocohsfhs-mainz.de (F.B.) 23 July 2021  
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Kinect sensor and pixel classification can be found in [6]. The studies mentioned here either focus on the segmentation of one type of component (e.g., SMDs or THCs) or are designed for inspection tasks. The underlying datasets are based on clean and intact circuit boards. However, in the recycling sector there are no clean and intact circuit boards. Due to the processing at the landfill site, the objects are in the form of shredded parts and are correspondingly dirty and demolished. This fact makes the classification of components more difficult. Furthermore, these approaches do not cover the separation of different materials. For example, capacitors mounted on circuit boards differ not only in shape but also in their material composition. In order to be able to separate successfully in such cases, an approach based purely on depth information or images cannot be really effective. The use of physical information, on the other hand, offers much greater potential in terms of material classification. Hyperspectral Imaging (HSI) technologies enable the acquisition of physical information and can be easily integrated into sorting processes due to technological advancements in instrumentation engineering in recent years [7]. As examples, [8; 9] use spectral information for the classification of metals on PCBs. The use of HSI in combination with RGB images can be found in [10] and propose an object detection approach to detect recyclable objects and elements on PCBs. The authors used spectral properties of the objects (integrated circuits, gold connectors and electrolytic capacitors) recorded in the HSI to localize the objects whereas the spatial properties given by the RGB image are used to classify the localized objects. However, the results showed that not all components on the PCBs are detected. As a reason, the authors stated that this can be attributed to the very small training data set available and the parameters chosen for the prediction process. This explanation is plausible and describes the generally problem of machine learning methods. A comparison of some machine learning methods is conducted in [11] and shows that there is no method that consistently provides the best performance and that the quality of the prediction result mainly depends on factors such as the availability of training samples, processing requirements, tuning parameters and speed of the algorithm. In particular, the selection of training samples presents a problem due to the high variations with regard to PCBs that are demolished and contaminated. Rule-based approaches are an alternative to machine learning approaches as they need no prior training. They are based on expert knowledge and a prior analysis of the data.