Automated quality assurance is gaining popularity across application areas; however, automatization for monitoring and control of product quality in waste processing is still in its infancy. At the same time, research on this topic is scattered, limiting efficient implementation of already developed strategies and technologies across research and application areas. To this end, the current work describes a scoping review conducted to systematically map available sensor-based quality assurance technologies and research based on the PRISMA-ScR framework. Additionally, the current state of research and potential automatization strategies are described in the context of construction and demolition waste processing. The results show 31 different sensor types extracted from a collection of 364 works, which have varied popularity depending on the application. However, visual imaging and spectroscopy sensors in particular seem to be popular overall. Only five works describing quality control system implementation were found, of which three describe varying manufacturing applications. Most works found describe proof-of-concept quality prediction systems on a laboratory scale. Compared to other application areas, works regarding construction and demolition waste processing indicate that the area seems to be especially behind in terms of implementing visual imaging at higher technology readiness levels. Moreover, given the importance of reliable and detailed data on material quality to transform the construction sector into a sustainable one, future research on quality monitoring and control systems could therefore focus on the implementation on higher technology readiness levels and the inclusion of detailed descriptions on how these systems have been verified.

Waste electrical and electronic equipment (WEEE) represents a heterogeneous waste stream containing valuable non-ferrous metals, yet RGB-based characterization of pre-concentrated output fractions (6–10 mm) recovered from shredded WEEE remains underexplored. Therefore, in this study, we investigate the feasibility of particle-level instance segmentation and propose a process-oriented temporal assessment method. By linking end-to-end detection time (E2E) to particle residence time (PRT) within the sensor’s field of view, the method assesses the preliminary temporal suitability of deep learning (DL) models for future inline applications. For this reason, ten state-of-the-art DL-models (YOLO and Mask R-CNN) were evaluated on a novel particle-level WEEE benchmark dataset. In addition, we examine how visual material characteristics and input variability affect detection performance and the training process. Results demonstrate that YOLOv9 achieved an mAP@0.5 of 75.16% across all material classes, while E2E remained below the PRT, indicating inline feasibility for the investigated application. Replacing the ResNet-50 backbone of Mask R-CNN with VGG-16 reduced E2E from 26 ms to 21.1 ms (53.3%) and increased mAP@0.5 from 67.30% to 74.82%. Our work establishes groundwork for future inline applications, emphasizing the interaction between process constraints and detection time. Overall, these findings advance sensor-based real-time characterization of pre-concentrated fine fractions from shredded WEEE, contributing to future inline applications toward Recycling 4.0.