Advanced recognition systems for industrial environments using robot binocular vision and semantic segmentation

Abstract This paper presents a thorough description of a highly dependable object recognition model for industrial applications in difficult environments based on a combination of stereo vision, semantic segmentation and a hybrid CNN–V3 architecture; The pipeline performs pixel-level segmentation using DeepLabV3+, extracts RGB-D features with VGG19, and final classification is achieved through a concise CNN–ViT fusion module. Solid semantic segmentation and stereo depth-aware modelling are adaptable and effective solutions to perennial challenges in industrial recognition such as occlusion, lighting fluctuations, and complex backdrops that can undermine performance in industry. The proposed pipeline has the potential to enhance depth-based perception and spatial reasoning for identification and interaction with industrial objects in at least three modes: recognition, cognition, and classification. The model was evaluated through an experiment based on the XYZ-IBD dataset revealing a total accuracy of 96.54 %, F1 score of 0.956 and AUC of 0.996 demonstrably indicating significant advantage over existing 3D deep learning-based recognition models and those based on binocular images. The combined semantic segmentation and stereo depth approach offers a robust architecture that enhances perception and accuracy for Industry 4.0–driven industrial robotic applications. Performance gains were confirmed by comparing the model with baseline approaches such as the Bilateral Vision-Aided Transformer Network and binocular Mask R–CNN, where it achieved higher accuracy, F1-score, and AUC. The framework also introduces a compact RGB-D fusion design and hybrid CNN–ViT architecture that improves robustness and recognition reliability in complex industrial settings.