Software architecture for real-time hyperspectral analysis in material sorting systems

Abstract

The integration of hyperspectral imaging into industrial sorting systems has enabled high-precision classification of materials with similar visual characteristics but different chemical compositions. However, the real-time processing demands of HSI data acquisition, characterised by high spectral and spatial resolution, require advanced computational strategies. This paper presents a scalable and efficient software architecture designed for real-time hyperspectral analysis in automated material sorting lines. The architecture exploits heterogeneous and homo geneous parallelism to distribute pre-processing, classification and segmentation tasks across multiple threads and processing cores. Two classification methods, based on Spectral Angle Mapper and Artificial Neural Networks, are developed and evaluated, both show high accuracy in material identification, but they impact system scalability in different ways. Extensive performance tests show that the proposed framework meets strict timing constraints and maintains low-latency operation on standard multi-core CPU systems. The modular design of the system ensures adaptability to different hardware configurations and material types, supporting future scalability and integration into diverse industrial environments. The real-time constraint imposed by the camera’s maximum frame rate is 1.493𝑚𝑠. Thanks to the optimisations applied, the critical processes, pre-processing and classification, have been reduced to just over 30𝜇𝑠 each, consuming only about 5% of the available time and leaving almost 95% free for additional operations or performance enhancements. This results in a system that is scalable both from a computational perspective and in terms of increasing the overall performance of the industrial plant.