Deep Learning–Driven Visual Intelligence for Chemical Instrument Monitoring: A Scoping Review From an Expert Systems Perspective
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.