vision inspection machine

With the rapid implementation of artificial intelligence technology and the continuous development of the intelligent robot industry, visual inspection machines are unleashing even stronger vitality. The typical structure of visual inspection equipment design mainly consists of five parts, namely: lighting, lens, camera, image acquisition, and computing hardware units.   What is the visual inspection? Visual inspection system refers to the use of machine vision products (i.e. image capture devices, divided into CMOS and CCD) to convert the captured target into an image signal, which is transmitted to a dedicated image processing system and converted into a digital signal based on pixel distribution, brightness, color, and other information; The image system performs various operations on these signals to extract features of the target, and then controls the on-site equipment actions based on the discrimination results.     Customization process of visual system 1. Software Testing The cyclic process of ensuring the correctness of software processes and the correct application logic relationships, discovering vulnerabilities in the system, conducting research and development modifications, and testing verification. 2. Hardware testing Conduct hardware reliability testing on the hardware itself (aging testing, compatibility testing, failure rate testing) and the environment to determine whether the software can run in multiple hardware configuration environments. 3. Joint debugging test Test the software and hardware joint debugging function to verify the correctness of electrical and software signal communication logic, light source, camera and other hardware triggering functions such as photography and scanning, as well as the statistics of detection results. 4. Model testing   Focus on the functional testing, performance testing, evaluation of model indicators, and analysis of indicator results of the model.     How to carry out testing of visual inspection system? Customer Requirements Application type: Accurately and detailedly understand the changes in product testing standards, external dimensions, and other factors that affect testing, and preliminarily evaluate whether they can meet the requirements. Stage requirements: Customers' demands for visual inspection efficiency, quantifying the time required for visual inspection steps. Accuracy requirement: Control the accuracy of product defect detection. Installation space: Confirm if there are any restrictions on the installation of visual equipment in the on-site environment.   Conceptual design Requirement analysis: Organize key customer requirements and analyze their feasibility. Hardware design: Selection of visual system platform, camera, lens, and light source. Software design: Use third-party visual software or develop visual processing software independently. Feasibility verification: Set up software and hardware environments, customize human-computer interaction interfaces, and conduct preliminary testing to determine if they can meet customer needs.   Algorithm Deployment Cloud platform development: Collect product defect sample images, upload and store images, select images, annotate, upload, train, test, optimize, and apply.  

Appearance Defect Detection of Bottle Body The appearance defect inspection of the bottle body is a common problem in daily production. For manufacturers in industries such as pharmaceuticals, dairy products, alcoholic beverages, seasonings, and daily chemical products, due to the increasing demand for product packaging refinement, quality, and continuous batch production, the market has put forward higher standards for bottle factory quality testing. The emergence of machine vision inspection machines based on AI algorithms, it has become a new tool to assist enterprises in efficient production.   Vision Defect Inspection Solution of Bottle Body Based on the theory of computer vision and pattern recognition, and equipped with the AI edge computing unit developed by KeyeTech, it fully meets the computing power support under the requirements of high-definition and high-speed. It has the advantages of high computing power, high stability, and low power consumption, and optimizes the problem of computing power distribution in multi camera collaborative processing.   Software & Hardware Platform (1) Self developed high-precision CCD/CMOS industrial camera (camera+lens) (2) Self developed surface light sources, circular light sources, and other LED light sources (3) Semi supervised learning mode (4) Self developed AI edge computing unit, a high-performance embedded computing platform for industrial scenarios (5) Building an AI cloud training platform independently     Detection Contents Bottle defects: black spots, color difference, impurities, threads, lifting rings, notches, residues, burrs, bubbles, holes, uneven thickness, deformation, size, spray code, trademark, mold number, etc   Bottle material: PET, PE, PP, HDPE, PC, etc   Widely Applications KeyeTech AI visual defect inspection technology is widely used in industries such as pharmaceuticals, dairy products, seasonings, alcoholic beverages, and daily chemicals. Solution Advantages High efficiency Self developed AI edge computing unit has faster speed, lower power consumption, stronger continuous computing capability, no power failure at high temperature, and stable operation.   High Integration Integrating light, machinery, electronics, computing, and software, we have built an AI platform with higher integration, faster computation, and stronger processing capabilities.   Strong data compatibility The semi supervised learning mode effectively solves the problem of small data samples and difficult labeling.   High flexibility Support fast switching of detection scenarios   Easy to operate 3 minutes to get started, with 7X24 hours of remote operation and maintenance support.