Aug 2026· Expert systems· Vol 43· 0 citations· 54 references
TL;DR
A comprehensive scoping review of deep learning–driven visual intelligence for industrial applications, with a specific focus on chemical instrument monitoring from an expert systems perspective, and highlights the gap between high‐performing vision models and their reliable integration into expert systems for chemical operations.
Abstract
Automated visual recognition in industrial environments has become a key enabler for intelligent monitoring systems, particularly in safety‐critical domains such as chemical plants. Unlike general industrial vision tasks, chemical instrument monitoring operates under stringent constraints, including harsh environments, legacy non‐digital devices, and high reliability requirements for decision‐making. In this context, deep learning‐based visual recognition should not be viewed as an isolated perception task, but rather as a critical component within expert systems that support operational decision‐making. This paper presents a comprehensive scoping review of deep learning–driven visual intelligence for industrial applications, with a specific focus on chemical instrument monitoring from an expert systems perspective. A total of 127 studies published between 2015 and 2025 are systematically analysed, covering object detection, image segmentation, and optical character recognition (OCR), along with their integration into practical monitoring pipelines. Beyond categorizing models, this review emphasizes the evolution from standalone perception models toward system‐level solutions that incorporate edge deployment, data‐efficient learning, and explainability. Key challenges are identified, including data scarcity due to proprietary industrial datasets, limited model generalization across dynamic environments, computational constraints in real‐time deployment, and the lack of interpretability required for safety‐critical decision support. Importantly, this review highlights the gap between high‐performing vision models and their reliable integration into expert systems for chemical operations. Emerging trends are discussed, including synthetic data generation, domain adaptation, foundation models (e.g., SAM and open‐vocabulary detection), and multimodal fusion, with a critical evaluation of their applicability and limitations in chemical instrument scenarios. By reframing industrial visual recognition within the broader context of expert systems, this work provides a structured understanding of current capabilities and outlines future directions for developing trustworthy, efficient, and human‐centric intelligent monitoring systems in chemical industries.
Image segmentation is a fundamental step in computer vision and a cornerstone of robotic perception, serving as the foundation for interpreting data acquired from vision sensors, enabling robots to analyze complex visual environments, identify and localize objects, and support intelligent decision-making and autonomous control. It plays a critical role in applications such as autonomous navigation, robotic manipulation, medical robotics, agricultural robotics, autonomous vehicles, and human–robot interaction. Image segmentation has evolved from classical methods, which relied on handcrafted rules and mathematical models, to deep learning approaches that learn complex visual patterns directly from data. This evolution reflects advances in algorithms, computational power, and the theoretical foundations of mathematics and data science. Modern deep learning methods rely heavily on large, well-annotated datasets to train sophisticated neural networks. Yet, classical techniques remain valuable in certain scenarios, offering faster, reliable results without extensive computational requirements. Understanding the strengths and limitations of both approaches is key to selecting the right method. This paper surveys image segmentation techniques, comparing them in terms of accuracy, computational cost, and processing speed to guide informed method selection.
The increasing need for automated quality control techniques has been driven by the rapid advancement of smart manufacturing systems within the Industry 4.0 framework. Machine vision using deep learning technologies has emerged as a key enabler of streamlined inspection and decision-making workflows across various production environments. This systematic review analyzes the evolution of deep learning models from traditional Convolutional Neural Networks (CNNs) to transformer-based architectures, examining how these developments address challenges in defect detection and classification on modern industrial production lines. Several key findings emerge from our analysis. First, while CNNs continue to dominate due to well-established performance in local feature extraction, transformer models demonstrate superior accuracy for complex defect geometries through global contextual reasoning, albeit at higher computational costs. Second, real-time detection methods such as YOLO have proven viable for high-velocity production environments, though they typically sacrifice accuracy for speed. Third, integration with edge computing, digital twins, and IoT infrastructure is essential for developing scalable quality control systems. Additionally, ongoing challenges include data format inconsistency, model interpretability, domain transfer limitations, and the gap between controlled development conditions and actual factory environments. This review concludes by outlining future research priorities: developing hybrid CNN-Transformer models, establishing standard evaluation criteria, creating resource-efficient edge applications, and building collaborative human-AI frameworks, thereby providing guidance for researchers and engineers advancing automated visual inspection systems.
Z. Mighouar, J. Melloui, Khalifa Mansouri et al.· Discover Mechanical Engineer...· 0 citations
A comparative evaluation of two distinct artificial intelligence paradigms for the analysis of optical microscope images of microplastics shows that the trained DL model excels in high-throughput, reproducible screening, while the VLM-HITL system offers enhanced interpretability and flexibility for ambiguous cases.
Mahmoud Sami, Sara El-Kerkary, Marina S. Fawzy· Scientific Reports· 0 citations
Knowledge-Guided Machine Learning (KGML) has emerged as an important paradigm for addressing the limitations of purely data-driven models by incorporating domain knowledge into the learning process. Although previous surveys have discussed KGML in broad terms, a comprehensive review dedicated specifically to computer vision remains lacking. To address this gap, this paper presents a systematic survey of KGML in computer vision based on a PRISMA-guided review of studies published between 2014 and 2025. From an initial pool of 2,788 publications, 286 high-quality studies were selected for detailed analysis. Based on this review, we propose a taxonomy that distinguishes between scientific knowledge and non-scientific knowledge, and further categorizes knowledge integration strategies into knowledge-guided learning, knowledge-guided architectures, and knowledge-guided pre-training. The survey shows that KGML has been applied across a wide range of computer vision tasks, including classification, object detection, segmentation, and multimodal understanding, with reported benefits in generalization, interpretability, and robustness. In addition, we identify key open challenges related to knowledge representation, fusion complexity, evaluation, and bias, and outline future research directions for advancing KGML in computer vision.
Unknown authors· Multimedia tools and applica...· 0 citations
Computer vision (CV) systems driven by artificial intelligence (AI) are increasingly replacing manual and conventional rule-based inspection procedures in industrial quality inspection, enabling automated, real-time, and data-driven decision-making based on visual data. Conventional inspection procedures are usually limited in terms of scalability, human-related errors, and high operational costs, which drives the increasing reliance on smart vision-based technologies. A practical, practice-oriented review of AI-based computer vision systems for industrial quality control is provided in this paper, with emphasis on real-world deployment issues and performance aspects. Two representative industrial case studies are examined. The first investigates real-time extrusion monitoring in robotic building construction, where geometric deviations, bead-width variation, surface irregularities, and process inconsistencies are detected during material deposition using vision-based monitoring and image-processing pipelines. The second case study focuses on automated inspection of bolts and screws in manufacturing lines, addressing presence detection, orientation recognition, and defect classification under high-speed production conditions. In both cases, widely adopted vision and AI techniques, including image-processing pipelines, convolutional neural networks, and edge-computing hardware, are discussed and compared. The analysis shows that AI-enabled computer vision systems can outperform traditional rule-based or manual solutions in terms of inspection accuracy, consistency, and throughput when they are supported by reliable acquisition, representative data, and robust industrial integration. Nevertheless, challenges related to dataset quality, model generalization, lighting variability, and real-time computational constraints remain critical in industrial environments. In conclusion, AI-based computer vision plays a central enabling role in intelligent quality inspection within the context of Industry 5.0. Future research should focus on adaptive model capabilities, tighter integration with cyber-physical systems, and scalable deployment strategies to achieve reliable and autonomous inspection across diverse industrial sectors.
Maaz A. Khan, C. Vasques, A. Cavadas· Encyclopedia· 0 citations
This review explores the key techniques for explainability in deep visual recognition, including model-agnostic methods such as LIME and SHAP, model-specific approaches like saliency maps and feature visualization, and intrinsically interpretable models like decision trees and rule-based systems.
Khalid N. Alharbi· Electronics· 0 citations
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