Traditional industrial manufacturing still relies mainly on manual inspection to detect defects on product surfaces due to limitations in science and technology. This method, limited by human labor and outdated technology, is not only slow and inefficient but also prone to errors, resulting in inaccurate inspection results.
In today's society, with the emergence and development of computer technology, artificial intelligence, and other scientific and technological advancements, as well as the deepening of research, surface defect detection technology based on machine vision has emerged. This technology greatly improves the efficiency of production operations, avoids the impact of working conditions and subjective judgment on the accuracy of detection results, and enables better and more precise surface defect detection, allowing for faster identification of product surface flaws.
Product surface defect detection is a type of machine vision technology. It utilizes computer vision to simulate human vision, acquiring, processing, and calculating images of physical objects, ultimately performing actual inspection, control, and application. Surface defect detection is a crucial part of machine vision inspection, and its accuracy directly impacts the final product quality. Since manual inspection methods are no longer sufficient to meet the demands of modern production processes, machine vision inspection has effectively overcome this limitation. The widespread application of surface defect detection systems has promoted high-quality production in factories and the development of intelligent automation in the manufacturing industry.
Machine vision intelligent inspection system
The application of surface defect detection systems improves the accuracy and efficiency of inspection. However, there are several steps to consider before conducting product surface inspection.
First, the texture image of the image surface must be acquired and analyzed using an image acquisition system;
Second, the acquired images are segmented step by step so that product surface defects can be classified according to their unique regional characteristics.
Third, further analyze the target area of the scratches within the above-mentioned classification areas to make the range more accurate and precise.
After the above three steps, the defect areas and features on the product surface can be further confirmed, thus completing the basic steps of surface defect detection.
Automated testing flowchart
Machine vision technology improves user productivity, enabling more refined production and clearer division of labor. Simultaneously, it reduces labor costs, saves financial resources, and achieves integrated development of machine intelligence.
What are the classifications of machine vision inspection technology?
To adapt to today's rapidly developing society, machine vision inspection technology is indispensable. In environments unsuitable for human work, machine vision can replace human labor. Machine vision inspection technology can be categorized as follows:
(1) Generally speaking, machine vision inspection technology can be distinguished according to its inspection function: positioning, defect detection, counting/loss detection, and dimensional measurement.
(2) Machine vision inspection technology can be divided into online inspection system and offline inspection system according to the carrier of its device.
(3) According to the difference in detection skills, there are generally stereo vision detection skills, spot detection skills, scale measurement skills, OCR skills, etc.
Machine vision inspection technology is crucial for eliminating defects such as flaws, blurriness, debris, or dents in products to ensure their functionality and performance. Therefore, it is now widely used in product defect detection and dimensional inspection across various industries. For example, vision systems can perform inspections on multiple aspects of a product; they can detect defects or misaligned pins in electronic components, measure the shape of syringe parts, or distinguish colors to check for incorrect installations.
Machine vision inspection technology has applications in various fields, including license plate recognition and traffic flow detection in the transportation industry, packaging inspection in the pharmaceutical industry, volume and outer packaging inspection in the beverage industry, cigarette label and outer packaging inspection in the tobacco industry, installation inspection in the automotive industry, print quality inspection in the printing industry, fabric defect detection in the textile industry, screw inspection in the hardware industry, cargo sorting in the transportation industry, fruit sorting in the food industry, welding inspection and installation positioning in the electronics industry, steel plate surface defect detection in the steel industry, smart meter reading, and smart meter measurement.
Machine vision inspection technology, based on machine vision image processing skills, automatically inspects all products. This plays a crucial role in controlling product quality and ensuring product quality, preventing defective products from leaving the market, and thus enhancing a company's core competitiveness. The company has achieved not only social benefits, but its machine vision inspection technology has also brought substantial economic benefits to numerous companies.
