In the field of machine vision, matching technology is one of the core technologies for target positioning, recognition, and tracking. From early pattern matching to geometric matching, contour matching, and today’s AI deep learning era, matching algorithms have evolved over decades. This paper systematically reviews the development history of this technology, discusses algorithmic paths for performance optimization, and re‑examines the value of traditional matching methods amid the AI wave.

The wave of artificial intelligence is sweeping across the globe at an unprecedented pace, fundamentally transforming the underlying logic of the job market. According to a McKinsey Global Institute report, by 2030, approximately 400 to 800 million people worldwide may lose their jobs due to automation, but simultaneously AI will create more high-value positions. The World Economic Forum's "Future of Jobs Report" indicates that by 2025, machines will undertake more work currently performed by humans, but this process will result in a net increase of 12 million jobs. This transformation is not a simple transfer of work, but a reconstruction of the entire industrial ecosystem.

Application of Machine Vision AI in Appearance Defect Detection Introduction Under the wave of Industry 4.0 and intelligent manufacturing, product quality control has become the core competitiveness of manufacturing enterprises. Traditional manual inspection methods suffer from numerous pain points such as low efficiency, high missed detection rates, and high costs, making them unable to meet […]