Online Visual Bottle Inspection Machine

High quality defect data is crucial for training and optimizing AI visual inspection models on industrial production lines. To obtain rare and high-quality defect samples for product appearance defect detection, multiple layers of effort are required. Artificially creating product defect samples - low authenticity! High yield rate and low defect rate, online collection of defect samples - time-consuming! Product changeover, collecting a large number of defect samples in a short period of time - difficult! Sample defects are complex and diverse, and collection is cumbersome - low efficiency!   Defect Generation By using AI visual inspection and diffusion modeling techniques, various types, positions, and shapes of defect images can be simulated with only a small number of sample images through forward and backward diffusion algorithms. Simulated defect images are highly similar in appearance and features to actual defects, providing an effective solution to the problem of defect data scarcity.   Significant role of key nodes Rapid model construction-Rare defect samples are few, and defects can be generated through "defect synthesis" to achieve rapid model construction in AI visual inspection. Rapid deployment of models-Product changeover can utilize "defect synthesis" to quickly generate defects in related products for model training and rapid deployment. Rapid improvement of the model-When there are missed defects on the production line, simulation defects can be generated through "defect synthesis" to quickly reduce production missed defects.   Intelligent, efficient, and easy to operate Defect synthesis "only requires three steps of marking defective samples, placing good products, and defect synthesis to generate a large number of high-quality defect maps, expand the training sample set, significantly shorten sample collection time, and achieve fast model training. Strong performance and outstanding advantages Strong applicability- Can be used for products and different defects in multiple industry sectors Strong flexibility- Independently choose defect location, quantity, and type to meet personalized user needs Easy to operate- Three step generation of defect maps, significantly saving time and cost High Collaboration- The generated results come with annotation information, without the need for secondary annotation, and can be directly used for model training Excellent effect- The generated defects are highly similar to real defects, greatly improving the training effectiveness of the model in AI visual inspection field.   Efficient generation of simulation defects Bottle cap: logo print Bottle cap: stains Bottle cap: black spot Generative AI plays an important role in the field of industrial vision. Through intelligent defect generation technology, KeyeTech Skill quickly generates a large number of defect images that are close to reality, solving the problem of scarce defect samples and time-consuming and laborious collection, greatly improving the training efficiency and generalization ability of AI visual inspection model! Click here for more AI VISUAL INSPECTION VIDEOS

In recent years, the concept of AI has been widely accepted by the public, and AI visual inspection is gradually being applied in the industrial sector for the detection of product appearance defects. Appearance defects in products can negatively impact aesthetics, comfort, and overall performance. This is why manufacturing companies are keen to detect these flaws promptly, allowing them to control quality and enhance the product's added value. While the importance of algorithms in AI visual inspection is often emphasized, the lighting source directly affects image quality. The primary role of the lighting in AI visual inspection equipment is to overcome the interference from ambient light, ensuring image stability and achieving the highest possible contrast. High-contrast imaging makes it easier to detect product defects. At KeyeTech, our AI image recognition equipment not only employs deep learning-based algorithms but also features our self-developed optical imaging system.   Design of Imaging Angles and Intensity in 3D Space Currently, our AI image recognition devices utilize an optical imaging system where the lighting source configurations are categorized based on illumination methods: bright field and dark field illumination, structured light and strobe light. These methods work in conjunction with both the surface and internal characteristics of the objects being inspected, enabling comprehensive relational analysis. By projecting the light source onto the inspected objects and analyzing the distortions produced, we can demodulate the 3D information of the objects, allowing for precise location of product defects.   Multispectral Imaging from Ultraviolet to Infrared KeyeTech's optical imaging system employs a full spectrum of ultraviolet to infrared, including polarized and fluorescent methods, to achieve comprehensive 360° detection of the objects. Defects invisible to the naked eye can be revealed with complete clarity, ensuring no blind spots or overlooked areas.   Energy-Efficient and Durable Our optical imaging system uses LED lighting, which is compact, energy-efficient, and features fast response times. It also offers excellent monochromaticity, high reliability, and uniform, stable light output, making it easy to integrate for optimal imaging results on products.