machine vision inspection

When encountering defects in bottles, lids, straws, etc What testing methods do you know? Human eye recognition? leak hunting? But do you know that this method has a very high false detection rate? Is there a safe and efficient detection method? A group of innovative pioneers have found the answer AI visual defect detection From manual checking to traditional vision inspection, and then to AI, the revolution of defect detection technology Before the AI visual defect inspection scheme was developed, the defect detection of packaging containers, such as black spots, dirt, flash edges, and missing materials on beverage bottles, mainly relied on manual inspection. Faced with high-intensity repetitive work, manual inspection inevitably leads to errors, and when new defect problems arise, it is difficult for people to effectively and quickly identify them. However, the market environment is fiercely competitive, and even 0.1% of product defects are not allowed. In order to ensure high quality of products in production, more efficient defect detection solutions must be used.   Faced with the urgent demand of the market, Keye Technology has focused on segmented fields in the past decade and successfully developed diversified AI visual defect detection solutions, leading the world in defect detection technology.   KeyeTech, a professional partner in segmented vision fields Compared to traditional detection solutions, Keye adopts AI visual defect detection. When faced with new defect detection needs, there is no need to replace the equipment. Instead, the new defects need to be sampled, labeled, and trained, and the data is uploaded to the cloud platform to develop detection templates. When in use, the corresponding detection templates can be retrieved to ensure stable production.   In addition to having detection functions such as black spots, dirt, burrs, missing materials, and factory model numbers, the Keye AI visual defect detection equipment can also count and process qualified products. At the same time, it is also compatible with defect detection of different sizes of bottle caps, truly achieving efficient work with just one device.     Nowadays, more than 2000 companies around the world use Keye's AI visual defect detection equipment. Behind this achievement is not only the result of Keye's ultimate pursuit of technology, but also a reflection of KeyeTech's strong sense of social responsibility. Get more videos of KeyeTech in Youtube.  

There are currently three inspection methods in the production of plastic packaging containers. The first is traditional manual inspection, which detects defects in the product through eye observation. The second is machine vision inspection, which is based on traditional algorithms. The third is the latest AI algorithm visual inspection system. With the increasing quality requirements for packaging products in the global industry, the efficiency of defect inspection will also become more stringent. Below we will compare several existing testing methods, which will help people find the appropriate testing method to better meet quality requirements and reduce enterprise operating costs.     Due to subjective factors, low efficiency, and susceptibility to fatigue, manual vision inspection cannot guarantee the efficiency and long-term stability. Traditional algorithms vision inspection have many parameters and rely heavily on professional debugging personnel. Poor adaptability, high false detection rate while ensuring detection accuracy, resulting in low detection efficiency. Deep learning AI vision inspection system enables machines to learn the inherent patterns and representation levels of sample data, enabling them to have the ability to analyze, learn, and reason logically like humans. Excellent long-term performance and stability, with efficient detection accuracy.     Human visual inspection has a relatively low recognition rate for colors, which is easily influenced by human psychology and cannot be quantified. Then, machine detection color discrimination can be quantified. For example, human eyes can only recognize 64 grayscale, and machines have strong grayscale recognition ability. Currently, 256 grayscale levels are generally used, and the acquisition system can have grayscale levels such as 10 bit, 12 bit, and 16 bit. The resolution of the eyes is poor, and they cannot view small targets with high resolution. Machines can observe targets at the micrometer level, but the human eye has a slow observation speed. The 0.1 second visual persistence makes it difficult for the human eye to see fast-moving targets clearly. On the other hand, machines have a fast speed, with a shutter time of about 10 microseconds and a high-speed camera frame rate of over 1000. The processor speed is getting faster, and the human eye range is narrow. Visible light devices in the 400nm-750nm range have a wide detection range, ranging from ultraviolet to infrared spectra. Human visual inspection has poor adaptability to the environment, and there are many situations that can cause harm to people. Machine vision inspection has strong adaptability to the environment, and protective devices can also be added. Human eye detection has low accuracy and cannot be quantified. Machine vision has high accuracy and can reach the micrometer level, making it easy to quantify. Relying on human detection also has other subjectivity, psychological influence, and fatigue.     From the above data and analysis, it can be seen that replacing human visual inspection with machine vision inspection will be a trend, especially with the continuous increase in labor costs worldwide. Whether it is from the perspective of production costs, management standards, or detection efficiency, the new generation of AI algorithm visual inspection will be favored by the market. Currently, the visual inspection system supported by the latest generation of AI algorithm by Keye has been increasingly recognized by more customers in domestic and international markets, and has become a leading enterprise in the plastic bottle, cap, printing and other industries. At the same time, it has played a good role in promoting the real landing of artificial intelligence in the packaging inspection market.    

Machine vision inspection is a rapidly developing branch of artificial intelligence (AI). According to the definitions of machine vision by the Machine Vision Division of the Society of Manufacturing Engineers (SME) and the Automation Vision Division of the Robotics Industry Association (RIA), machine vision is a device that automatically receives and processes an image of a real object through optical devices and non-contact sensors to obtain the required information or to control robot motion.   Simply speaking, machine vision is using machines instead of human eyes. Machine vision simulates the eyes for image acquisition, extracts information through image recognition and processing, and finally completes the operation through the execution device.   Traditional machine vision inspection technology requires representing data as a set of features or inputting them into a prediction model to obtain prediction results. This requires completing specific actions, making it difficult to adapt to future flexible production needs, especially in scenarios where defect types are complex, subtle, and background noise is becoming increasingly difficult to apply.     After being equipped with AI deep learning function, machine vision converts the original data features into a higher-level and more abstract feature representation through multi-step feature transformation, and further inputs it into the prediction function to obtain the final result.     Machine vision based on deep learning can combine the efficiency of machine vision with the flexibility of human vision in an ideal state, thus completing detection in increasingly complex environments, especially when involving deviations or extreme environments, meeting the stringent requirements of downstream for defect accuracy and universality.