Aug 2026· AI for Engineering· 0 citations· 81 references
TL;DR
This survey categorizes AI-based vision systems by their functional objectives rather than algorithmic classification, and organizes prior work by functional roles and practical deployment considerations, rather than algorithm-centric evaluations, to provide practitioners with actionable insights for industrial adoption.
Abstract
Recent advances in artificial intelligence (AI) and computer vision technologies have enabled practical applications in industrial environments where safety, quality, and productivity are critical. To support both researchers and practitioners, this survey categorizes AI-based vision systems by their functional objectives rather than algorithmic classification. Specifically, representative implementations are organized across key application areas including safety monitoring, product quality inspection, assembly line support, and worker productivity enhancement. Most of the surveyed studies are in the manufacturing and construction sectors, where real-world deployments have demonstrated measurable improvements. Unlike many previous reviews, this survey focuses on image-centric applications, using visually interpretable outputs such as photographs, video frames, and real-world examples to illustrate the on-site usability of AI vision systems. It also organizes prior work by functional roles and practical deployment considerations, rather than algorithm-centric evaluations, to provide practitioners with actionable insights for industrial adoption.
This work aims to establish a unified perspective on vision-based mistake analysis in procedural activities, highlighting its potential across diverse domains and aspects and categorizing approaches based on their use of procedural structure, supervision levels and learning strategies.
Konstantinos Bacharidis, Antonis A. Argyros, Hazel Doughty· 0 citations
Suggestions for ensuring safe person detection using AI in industrial environments are offered, including suggestions for ensuring safe person detection using AI in industrial environments.
Iwo Kurzidem, Andrea Matic-Flierl, Poulami Sinhamahapatra et al.· 0 citations
Validation at demonstrator scale confirmed the system’s ability to meet the initially defined specifications, demonstrating reliable detection of both defects and organic residues, including those caused by impacts and insect accumulation on aircraft surfaces.
M. Acebes, Marcos Alvarez Garcia, Antonio Grande Ferreiro et al.· e-Journal of Nondestructive...· 0 citations
An AI-enabled perceptual alignment framework for spherical surface inspection is proposed, integrating skeleton-based feature matching with three-dimensional pose estimation to mitigate perceptual uncertainty in high-precision manufacturing environments and demonstrates how AI mediated perception can standardize judgment.
Hsiao- Ching Chang· International Journal of Bus...· 0 citations
This paper provides a structured and comprehensive overview of the fundamental concepts, technologies, and methods of Artificial Intelligence in the context of industrial production and presents current research at Hochschule Bochum.
Haris Karic, D. Mohr, Arockia Selvakumar Arockiadoss et al.· AHFE International· 0 citations
Automatic recognition and classification of vehicle damages is an important research direction in modern computer vision and artificial intelligence, playing an increasingly significant role in industrial and practical applications. Traditional computer vision-based approaches can recognize and classify objects with high accuracy; however, achieving task-specific performance typically requires large amounts of annotated data, time-consuming training or fine-tuning, and extensive parameter optimization. This process is not only resource- and cost-intensive but also limits the rapid adaptability of the technology. The aim of this research is to investigate how effectively the latest Multimodal Large Language Models (MLLMs) can recognize types of vehicle damage in a zero-shot setting, i.e., without fine-tuning, and to evaluate how their performance can be further improved through prompt engineering and fewshot prompting. MLLMs have the advantage of being able to provide multiple forms of information from a single query and supplement their outputs with natural-language explanations. In contrast, traditional models are generally designed to perform only one predefined task. Therefore, within the framework of this project, the performance of a fine-tuned YOLO-based computer vision model is compared with that of MLLMs in vehicle damage classification. This comparison highlights a modern, data-efficient approach that achieves competitive performance through prompt engineering and in-context learning, eliminating the need for additional model training and opening new directions for automotive applications.
Márk Mitrenga, B. Kővári, Péter Gáspár· International Conference on...· 0 citations
