The Development History and Frontier Paradigms of Industrial Visual Quality Inspection Empowered by Deep Learning

School of Resources and Safety Engineering, Central South University, Changsha, Hunan Province, China

^* Corresponding author: 8210242429@csu.edu.cn

Abstract

Industrial visual quality inspection technology has achieved breakthroughs in four technological levels: manual inspection, traditional machine vision, deep learning, and multimodal intelligence. It has gradually shed human dependence and moved towards intelligent, autonomous decision-making. Traditional methods, constrained by human subjectivity and rigid rules, are unable to cope with the complexity of industrial scenarios. A deep learning solution combining convolutional neural networks with an object detection framework enables automatic defect identification and classification, making defect detection more accurate and faster. Multimodal solutions utilize composite data inputs, including visual features and acoustic cues, to optimize system fault tolerance. Generative AI effectively compensates for the lack of rare defect samples. Lightweight models and edge-cloud collaboration facilitate the implementation of technology in real- time environments. Industrial quality inspection systems are forming a tightly integrated intelligent closed-loop system of “perception- understanding-action,” achieving a systematic upgrade from passive response to active intervention. By summarizing the technological evolution and key innovations, this paper seeks to promote deeper integration of AI and industrial inspection, paving the way for more adaptive and autonomous quality control systems.