01 Mainstream Technology of Machine Vision

Machine vision technology is an important branch in the field of automation and intelligence. Its mainstream technologies include image processing, deep learning, sensor technology, etc. Image processing technology is the core of machine vision, including digital image processing, image analysis, image understanding and other links. Through this series of processing processes, the machine has the ability to "see". Deep learning is one of the most challenging fields in machine vision. Its application range is wide, such as target detection, image segmentation, face recognition, etc. Through the training of deep learning algorithms, machines can achieve more accurate and rapid visual recognition. Sensor technology is an important means to achieve machine visual perception, mainly including optoelectronic devices, cameras, light sources and other types. The wide application of sensor technology in machine vision enables machines to better perceive changes in the environment, thereby realizing various applications in the field of automation and intelligence.

The mainstream technologies of image processing can include the following:

Digital image processing : This is the foundation of machine vision, involving the digital acquisition, encoding, transmission, storage and recovery of images. Digital image processing can perform a variety of operations on images, such as adjusting image brightness, contrast and color balance, filtering and denoising, edge detection, binarization, etc., so as to optimize image quality and extract more accurate information.

Image analysis : This is an important part of machine vision technology, including the analysis of image features such as shape, color, texture, and the extraction and recognition of these features. Image analysis technology can help machines understand the content of images and provide a basis for subsequent decision-making.

Image understanding : This is an advanced stage of machine vision, which enables machines to understand the actual content of an image through features extracted through image analysis, combined with technologies such as deep learning. For example, a model trained with a deep learning algorithm can identify people, objects, scenes, etc. in an image.

Pattern recognition : Pattern recognition is one of the important branches of image processing. It mainly identifies or classifies data by analyzing and processing the features of input data. In image processing, pattern recognition can be used to identify various patterns in images, such as faces, gestures, text, etc.

Computer vision : Computer vision is an important branch of machine vision, which mainly studies how to enable computers to understand and interpret scenes and objects in the real world through visual information such as images or videos. Computer vision has a wide range of applications, including security monitoring, intelligent driving, medical diagnosis and many other fields.

These technologies are interrelated to form a complete machine vision system. With the continuous development of science and technology, these mainstream technologies are also constantly improving and improving, providing support for more application scenarios.

02 Current mature application scenarios of machine vision

The relatively mature application scenarios of machine vision are:

(1) Production line automation : In the manufacturing industry, machine vision is widely used in production line automation. Through high-precision cameras and image processing technology, products can be automatically inspected, identified, and classified to improve production efficiency and product quality.

(2) Logistics sorting : Machine vision technology can help the logistics industry achieve fast and accurate item sorting. Through image processing technology, the machine can recognize the shape, color, text and other information of the item and automatically sort the item to the correct shelf or transport vehicle.

(3) Security monitoring : The application of machine vision in the field of security monitoring is becoming more and more widespread. Through the intelligent video monitoring system, real-time monitoring of the target area, target tracking, anomaly detection and other functions can be realized, thus improving the level of security prevention.

(4) Medical diagnosis : Machine vision technology can be applied to medical image analysis. Through image processing and deep learning technology, it can assist doctors in automatically analyzing and diagnosing medical images such as CT and MRI, thereby improving diagnostic efficiency and accuracy.

(5) Agricultural applications : In the agricultural field, machine vision technology can help realize crop status monitoring, precise fertilization, automated planting, etc., improve production efficiency and reduce costs. At the same time, machine vision can also be used for agricultural product quality inspection, automatically inspecting and classifying the appearance and defects of agricultural products.

The success stories and value of these application scenarios are mainly reflected in improving efficiency, reducing costs, and improving quality. The application of machine vision technology can significantly reduce manual intervention and improve the level of automation, thereby reducing production costs and improving work efficiency. At the same time, machine vision technology can achieve rapid and accurate identification and classification of items, improving logistics sorting efficiency and quality. In the field of medical diagnosis, the application of machine vision technology can improve diagnostic efficiency and accuracy and reduce medical costs. In the agricultural field, the application of machine vision technology can improve agricultural production efficiency and reduce costs, while improving the quality of agricultural products.

03 Scenarios suitable for machine vision in the industrial field

Machine vision has many mature application scenarios in the industrial field. The following are the scenarios suitable for machine vision and their specific descriptions:

(1) Scenario 1: Production line automation

In the manufacturing industry, machine vision is widely used in production line automation. Through high-precision cameras and image processing technology, products can be automatically inspected, identified, and classified to improve production efficiency and product quality. For example, machine vision can detect surface defects, sizes, and shapes of products, identify product types and label information, and thus achieve intelligent inspection and classification of automated production lines.

Solution: Adopt high-precision cameras and image processing technology, customize and develop corresponding algorithms and models according to different product characteristics and detection requirements, and realize efficient detection and classification of automated production lines.

Value: Improve production efficiency, reduce manual inspection costs, and ensure product quality and consistency.

(2) Scenario 2: Intelligent Quality Inspection

Machine vision can be applied to intelligent quality inspection of products. By collecting images or video information of products, it can automatically detect whether the products have defects or quality problems. For example, in automobile manufacturing, machine vision can detect whether the paint surface of the car body is smooth and whether there are scratches.

Solution: Use image processing and deep learning technology to train the model to learn product quality standards, and automatically detect and classify products by comparing actual product images with standard images.

Value: Improve product quality inspection efficiency and accuracy, reduce manual inspection costs, detect and handle quality problems early, and improve product quality and market competitiveness.

(3) Scenario 3: Automated Warehouse Management

Machine vision can be applied to automated warehouse management, and can achieve fast and accurate classification and inventory management of goods by identifying the shape, color, text and other information of goods. For example, in an e-commerce warehouse, machine vision can automatically identify the shape and size of goods and automatically classify them into corresponding shelves.

Solution: Use high-precision cameras and deep learning technology to train the model to learn the characteristics and classification standards of goods. By comparing the differences between goods and standard classifications, automatic sorting and classification of goods can be achieved.

Value: Improve warehouse management efficiency and accuracy, reduce manual sorting costs, and achieve fast and accurate inventory management and order processing.

(4) Scenario 4: Robot Navigation

Machine vision can be applied to robot navigation and positioning. By identifying features and signs in the environment, the robot can achieve autonomous navigation and obstacle avoidance. For example, in unmanned vehicles, machine vision can identify lane lines and traffic signs on the road to achieve autonomous navigation and obstacle avoidance.

Solution: Use image processing and deep learning technology to train the model to learn the characteristics and identification of the environment, and achieve autonomous navigation and obstacle avoidance of the robot by comparing the actual environment with the standard environment.

Value: Improve the efficiency and accuracy of robot navigation and positioning, reduce manual operation costs, and enable robots to replace humans to complete various tasks.

(5) Scenario 5: Equipment defects and prevention

Machine vision can be used to detect and prevent equipment defects. By collecting equipment images or video information, it can automatically detect whether the equipment has defects or potential failures. For example, in the blade inspection of wind power generation equipment, machine vision can detect whether the blade has cracks, damage, etc.

Solution: Use image processing and deep learning technology to train the model to learn the characteristics and quality standards of the equipment, and compare the differences between the actual equipment image and the standard image to achieve automatic detection and classification of the equipment. At the same time, combine other sensor data to analyze and predict equipment performance and detect potential problems early.

Value: Improve equipment detection efficiency and accuracy, reduce manual detection costs, detect potential equipment problems early and take preventive measures to improve equipment life and safety.

04 Application process of machine vision in the industrial field

1. Preparation

• Determine application scenarios and goals: Identify industrial scenarios for machine vision applications, such as production line automation, logistics sorting, etc., and determine the goals that need to be achieved through machine vision, such as product inspection, classification, and identification.

• Hardware selection: Choose appropriate hardware equipment such as cameras, light sources, lenses, etc. according to the application scenario and objectives. Factors such as equipment performance, accuracy, and stability need to be considered.

2. System design

• Algorithm selection: Select appropriate image processing and deep learning algorithms, such as digital image processing, image analysis, image understanding, pattern recognition, etc., based on the application scenario and objectives.

• System architecture design: Design the architecture of the machine vision system, including hardware, algorithms, software and other components, and determine the input and output of the system.

3. System Implementation

• Hardware connection: Connect the selected hardware devices according to the system architecture requirements and debug the working status of the devices.

• Software programming: Use programming languages ​​and development tools to write software programs for machine vision systems to implement image acquisition, processing, analysis, and recognition functions.

• Algorithm training: For specific application scenarios, use large amounts of data to train deep learning algorithms to improve the accuracy and efficiency of machine vision systems.

4. System testing

• Simulation test: Simulate application scenarios in real scenes to test the accuracy and stability of the machine vision system.

• On-site testing: Deploy the machine vision system to the actual production site for actual operation testing, and optimize and improve the system based on the test results.

5. Maintenance and optimization

• System maintenance: Regularly check the status of hardware devices to ensure system stability and reliability.

• Algorithm optimization: Based on actual application conditions and feedback, optimize and improve the deep learning algorithm to improve the performance and accuracy of the system.

A few points to note:

• Hardware selection should take into account the needs of the actual application scenario and choose appropriate equipment accuracy and performance.

• The selection of algorithms should take into account the characteristics and actual needs of the application scenario, as well as the scale and quality of the data.

• System design should take into account the system's scalability and stability, as well as its simplicity and ease of operation.

• System implementation should focus on program debugging and testing to ensure system stability and accuracy.

• System maintenance and optimization should focus on data collection and analysis, as well as continuous improvement and optimization of deep learning algorithms.

05 Three major application values ​​of machine vision in the industrial field

1. Improve production efficiency: Machine vision technology can automatically detect, classify and identify products on the production line, thereby reducing manual intervention and error rates. At the same time, through real-time data analysis and predictive maintenance, it can improve equipment reliability and maintenance efficiency and reduce downtime.

2. Improve product quality: Machine vision technology can detect and identify products with high precision, thereby ensuring product quality and consistency. Through machine vision technology, rapid feedback and adjustments can be achieved on automated production lines, problems can be discovered and solved in a timely manner, and product quality and customer satisfaction can be improved.

3. Reduce costs: Machine vision technology can reduce production costs and waste of human resources. Through automated and intelligent production methods, it can reduce manual intervention and error rates, reduce defective and scrap rates, and improve material utilization and energy efficiency, thereby achieving cost optimization and sustainable production.

来源： Digital Consultant Huang Junlei