A Deep Learning and Image Processing Pipeline for Object Characterization in Firm Operations.

This article focuses on a novel pipeline method combining deep learning and classic image processing techniques to automate object counting and layer characterization in firm operations, aiming to improve operational efficiency and reduce labor costs. The proposed approach uses a convolutional U-Net deep neural network (DNN) module to segment relevant objects from complex images, followed by a signal processing module—such as the MUSIC algorithm for counting horizontal layers or the watershed transform for separating overlapping objects—to accurately quantify items like stacked cardboard sheets and wood logs. Evaluations with real-world data from multiple manufacturers and retailers demonstrate that this pipeline method achieves over 93% accuracy with relatively small labeled data sets, outperforming end-to-end DNN-only models that require extensive training data. The method is interpretable, modular, cost-effective, and adaptable to diverse operational contexts, offering practical advantages for inventory control, production monitoring, and supply chain management.