Views: 0 Author: Site Editor Publish Time: 2025-06-09 Origin: Site
With the continuous deepening of industrial automation and intelligent manufacturing, machine vision systems have gradually become key production equipment for quality control and process optimization in production sites. However, for many users, how to scientifically evaluate the performance and accuracy of the system when selecting, testing or importing machine vision projects is still a key factor affecting the success of deployment.
The following will focus on the equipment evaluation standards. Production test methods and practical application points help companies judge whether a set of machine vision equipment is truly "practical" and "reliable" from a more professional and practical perspective.
I. Core dimensions of performance evaluation
To evaluate a machine vision system, it is usually necessary to start from five dimensions: imaging quality, inspection speed, recognition accuracy, environmental stability and system compatibility:
1. Imaging quality
The image is the basis of the visual system. When evaluating, the following indicators should be paid attention to:
Resolution and clarity: whether it meets the pixel density required for detail inspection;
Lighting and contrast: whether the image is bright and uniform, and whether the contour is clear;
Distortion and sharpness control: whether the lens has sufficient optical quality.
High-quality images can not only improve the recognition effect of the algorithm, but also provide guarantee for subsequent data processing.
2. Recognition accuracy
Accuracy is usually used to measure the performance of visual algorithms in actual production. Evaluation methods include:
Defect recognition accuracy rate (TPR)
False positive rate (FPR) and missed positive rate (FNR)
OCR recognition rate or barcode reading success rate
You can use sensors to simulate working conditions and continuously import sample images for verification. It is recommended to collect as many test samples as possible to ensure that multiple conditions are statistically analyzed.
3. Processing speed
Whether the system can meet the beat requirements is one of the keys to the evaluation. Generally includes:
Image acquisition speed (frame rate)
Image processing time (ms)
Whole machine response and output time
In actual projects, it is recommended to conduct actual measurements based on the total time of "the whole system from acquisition to output results".
4. Stability and anti-interference
Evaluate whether the system can run stably for a long time in a real industrial environment, focusing on:
Adaptability to changes in ambient light
Stable performance under temperature, humidity, vibration, and electromagnetic interference
Whether there is a freeze or recognition deviation during long-term continuous operation
For example, in a welding workshop, oily environment, or vibration production line, it is recommended to do a real-scene verification test.
5. System openness and compatibility
Machine vision systems often need to be connected to PLC, robots, databases or MES systems, and attention should be paid to:
Whether standard protocols are supported (such as GigE Vision, USB3 Vision, Modbus, OPC UA, etc.);
Whether SDK is provided for secondary development;
Whether images support multi-format export and platform connection (such as Halcon, VisionPro, LabVIEW, etc.);
Systems with strong compatibility are conducive to future expansion and maintenance.
II. How to build an effective performance test plan?
To scientifically evaluate the performance of visual systems, it is recommended that companies set up the following processes during the introduction stage:
Clearly define the detection goals and accuracy requirements: such as dimensional accuracy, character recognition rate, etc.;
Establish a control sample library: including normal products, typical defective products, and borderline cases;
Build a test platform: simulate actual working conditions for continuous operation;
Collect statistical data: accuracy, response time, abnormal conditions, etc.;
Periodic retesting and optimization: continuously adjust parameters and algorithms based on test results;
Through real sample and working condition testing, the solution that best meets the needs can be effectively screened out.
III. Case reference: Character detection project in the electronics industry
The production system of the electronics factory introduced an intelligent vision system to identify battery shell characters. Customers mainly focus on character integrity and recognition error rate. After testing the visual system, the following is the total result of data generation:
Average recognition accuracy: 99.2%
Error recognition rate: <0.3%
Whole image recognition response time: 80ms
Character contrast adaptive adjustment function, pass rate increased by 15%
At the same time, the system supports seamless access to the customer's MES system to achieve real-time upload of recognition results and abnormal tracing
Machine vision is not a device that can be used after simple installation. The performance and accuracy of the system directly affect the final application effect. Only through standard testing methods, real application scenario simulation and multi-dimensional indicator evaluation can enterprises accurately select and reduce trial and error costs.
Zhixiang Shijue continues to provide customers with high-performance and high-stability machine vision solutions, and formulates test plans in accordance with user needs, providing one-stop services from sample testing, algorithm optimization to system integration. Welcome to contact us for sample testing and customized evaluation service support.