CKVision | Competency Framework & Practical Guidelines for Programmers in the Era of AI-Assisted Programming Version: V1.0 | Release Year: 2026 Target Audience: All R&D Engineers of CKVISION Preface Over the past two years, AI coding tools including GitHub Copilot, Cursor and Claude Code have gained widespread adoption. They can generate hundreds of functional codes […]

China’s machine vision industry has achieved explosive growth over the past 15 years. In the early stage, the sector relied almost entirely on imported equipment and algorithms. Today, a number of competitive domestic enterprises including Hikrobot, Opt Machine Vision, TZTEK and CKVISION have emerged, pushing China’s domestic market scale to the forefront globally.

This article explores the core value of DM codes in the laser marking industry: through CKVision SDK's machine vision recognition algorithms and image preprocessing technology, it effectively resolves reading challenges such as metal reflections; implements quality grading based on ISO standards to monitor and optimize laser processes in real-time; and combines "on-the-fly" capture with coordinate calibration technology to significantly enhance production line capacity and alignment accuracy while ensuring full lifecycle traceability.

OCR and OCV Technologies: From Optical Recognition to Intelligent Visual Inspection Ⅰ. Origins and Early Development OCR (Optical Character Recognition) traces its history back to the early 20th century. In 1914, physicist Emanuel Goldberg invented a machine capable of reading characters and converting them into telegraph code, considered the prototype of OCR technology. In 1929, […]

In machine vision systems, image quality directly affects the accuracy and reliability of subsequent processing tasks. During image acquisition, transmission, and storage, images inevitably suffer from various types of noise contamination, including sensor thermal noise, quantization noise, and transmission interference. These noise sources severely degrade image quality and impact the performance of critical algorithms such as feature extraction, object recognition, and edge detection. Image filtering, as a core technology in machine vision preprocessing, aims to suppress noise while preserving useful information in images, such as edges, textures, corner points, and other important features.

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 […]

In today's era of intelligent manufacturing, machine vision technology has become an indispensable component of industrial automation. From precision assembly and inspection of electronic products to quality control in automotive manufacturing, from automatic sorting in food packaging to assisted diagnosis in medical imaging, machine vision technology is profoundly transforming traditional manufacturing. Behind these impressive applications, one core technology consistently plays a crucial role: geometric matching.