With the rapid development of industrial automation, machine vision technology has been applied in more and more fields. Especially in the detection and classification of fasteners, machine vision technology is gradually becoming the mainstream method. This paper introduces the design and implementation of an automatic fastener inspection and classification system based on machine vision.
Fundamentals of machine vision
Machine vision is a method of automatic detection and classification using image processing, feature extraction and machine learning. In machine vision systems, image acquisition is the first step, which obtains images of fasteners through high-resolution cameras and suitable lighting systems. Then, the image is preprocessed by image processing technology, including denoising, enhancement, binarization and segmentation, so as to facilitate feature extraction.
Design of fastener automatic detection and classification system
The design of this system mainly includes the following steps:
Image acquisition
In order to obtain accurate fastener images, we use high-resolution industrial cameras and appropriate lighting systems. In addition, in order to ensure the quality and stability of the image, we adopted a closed shooting environment to reduce the impact of external lighting and background noise.
Feature extraction
In the image processing stage, we use a variety of algorithms and technologies to extract the features of fasteners. These features include shape, size, color, and texture. Through the analysis and processing of these characteristics, we can obtain the key information of fasteners, and then classify them.
Classification algorithm
In the classification phase, we use a variety of machine learning algorithms to classify the characteristics of fasteners. These algorithms include support vector machines (SVM), neural networks (NN), and decision trees (DT). Through training and learning, these algorithms can automatically classify fasteners according to their characteristics.
System integration
Finally, we integrate each module and function together to form a complete set of automatic fastener inspection and classification system. The system can realize continuous and efficient detection and classification, which provides strong support for industrial production.
Experiment and result analysis
In order to verify the feasibility and effect of the system, we have carried out a series of experiments. First, we collected a large number of fastener sample images and used these images to train and test the system. The experimental results show that our system is more than 98% accurate in both detection and classification. In addition, we compared different classification algorithms to determine the best combination of algorithms. The experimental results show that the combination of multiple algorithms can further improve the classification accuracy.
Conclusion and prospect
This paper introduces the design and implementation of an automatic fastener inspection and classification system based on machine vision. The system can quickly and accurately detect and classify various types of fasteners. The experimental results show that the accuracy of the system exceeds 98%, which has high practical value and application prospect.
Going forward, we will continue to deeply study the application of machine vision technology in fastener inspection and classification. On the one hand, we will optimize the existing algorithms and techniques to improve the performance and efficiency of the system; On the other hand, we will explore new algorithms and techniques to achieve more complex and precise detection and classification tasks. In addition, we will also study how to combine machine vision technology with robotics technology to achieve automatic grasping and assembly of fasteners, and further improve the automation level and production efficiency of industrial production.
