In recent years, the global medical consumables market has continued to expand, GMP standards have gradually improved, and labor costs have also risen. In the process of drug production and packaging, traditional manual inspection methods have been difficult to meet the needs of production automation and quality control. With the continuous development of AI technology, new technologies for visual inspection of medical product production defects have emerged, bringing new hope and surprises to the production quality inspection of various medical products.
As one of the core technologies in the field of artificial intelligence, AI vision is the most direct source of information for machine vision systems. It uses vision sensors and computers to replace the human eye, and gives the machine the functions of segmentation, classification, recognition, tracking, and discrimination to determine the target of the human eye, so that the system has the ability to simulate the "mind map" of human beings, that is, the logic of human thinking. The use of deep learning technology can effectively solve the problems that are difficult to be solved by traditional algorithms in complex industrial application scenarios.
Traditional image processing relies on manual analysis of image features, extraction of features through image processing algorithms, and then distinguishing objects by key parameters of features. Feature extraction mainly relies on artificially designed feature extractors, which require specialized knowledge and complex modulation processes, and both methods are suitable for specific applications, and the generalization ability and robustness are poor. Traditional image algorithms can solve the image tasks of manual definition, design, and understanding of some specific scenarios, but they cannot meet the application requirements of multi-product production, multi-specification, dynamic operation of materials, diversified material attitudes, and diversified defect types.
The intelligent visual inspection system collects a large number of images, annotates the image data and trains a neural network, which can perform object-based inference. Its intelligence is mainly based on the data-driven feature extraction of deep learning, which is more efficient and accurate in the representation of the dataset, and the abstract features extracted are robust and generalizable, and can be end-to-end. The intelligent visual inspection system can solve the problems of image recognition and inspection that can only be described in a more advanced abstraction, with better versatility and no need for complex deployment. In the production process of the product, it can effectively detect all kinds of defects and defects and improve product quality.
Shenzhen Virtual Number Technology Co., Ltd. has been focusing on the machine vision industry, and has been continuously exploring and developing in the field of machine vision systems and machine vision software. The application scope involves packaging and printing, electronics, textiles, automobile manufacturing, semiconductors and other fields, providing a full set of visual solutions for all walks of life, such as machine vision products, visual automatic inspection technology, visual inspection equipment, visual positioning, visual alignment, visual measurement, defect detection, non-standard detection, label inspection, printing inspection, machine vision software, label quality inspection machine, etc.