Remote Control Odm Camera Inspection Machine For Shoes And Clothing Zipper Head

Remote Control Odm Camera Inspection Machine For Shoes And Clothing Zipper Head

Detection Principle

Brand clothing manufacturers constantly put forward new quality testing requirements for suppliers, traditional manual testing has been unable to meet the quality control requirements of large zipper manufacturers. The requirements for the size and appearance accuracy of zipper products are becoming higher and higher. A little careless may be inconsistent with the quality requirements of shoes and clothing companies, but also affect the brand reputation of zipper manufacturers, thus affecting the company's market position and economic benefits.

In the traditional manual detection method, it has been difficult to meet the detection requirements of the new zipper production process.Modern detection equipment is inseparable to guarantee high-quality products. The introduction of AI intelligent visual detection equipment is a necessary choice for the production of zipper heads.Now the core competitiveness among manufacturers is the competition of product quality. KEYE Technology combines the characteristics of the zipper puller, with its rich market experience in AI intelligent visual detection, uses the KEYE TECH AI+ deep learning algorithm, a strong industry knowledge base to solve the pain points of defect detection, and has strong compatibility;And achieved automatic detection of zipper head qualification.

Equipment Overvirews

The system has the characteristics of high detection accuracy, low missed detection and false detection rate. The software comes with statistics and defect image storage functions, real-time monitoring the operation of the production line, and timely detect the production line problems.

Visual detection equipment adopts image imaging technology, and uses machines instead of human eyes to measure and judge the detected objects. The device uses a high-pixel industrial camera to take a image of the zipper head and sends it to a dedicated GPU image processing system. According to the pixel distribution, brightness, color and other information, it is converted into a digital signal. The imaging system make various operations on these signals to extract the characteristics of the image, and then controls the action of the equipment on-site according to the discrimination results, than the unqualified products will be rejected. The entire detection process is automatic feeding, automatic detection, automatic sorting, and automatic unloading, making the detection work more efficient and faster.

1. Dimensions: height, length, width, number of angles, inner and outer diameter of the hole,

2. Damage: deformation of the diameter, uneven nozzle, burrs, burrs

3. Dyeing: stain, color inconsistency, cross color,

4. Coating: zinc damage

5. Pattern: missing characters, missing colors

6. The area is above 0.5 mm², and the area threshold can be adjusted.

7. The detection accuracy is higher than 99.5%, and the false rejection rate is less than 0.5%

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