Abstract
In order to solve the problems of slow manual inspection speed and low fault detection accuracy of car seat back parts, this article using Q company’s car seat back parts researches and designs a car seat back classification and quality inspection screening system. Firstly, SURF (speeded up robust features) is combined with the CNN (convolutional neural network) to classify three types of car seat backrests: A, B, and C. Then, to establish the spring hook angle detection model of the car seat back to detect the misfitting and omission of the Class A car seat back springs, experimental results showed that the neural network-based car seat back detection method proposed in this paper had a feature point mismatch rate, which is less than 1.5% in the classification and recognition of car seat backs. The recognition rate of the training sample was 100% and that of the test sample was 99.56%. The accuracy rate of detection when inspecting 50 car seat backrests reached 98%, and the test results showed that the system can effectively reduce labor costs and improve the detection efficiency of auto parts.
Highlights
Car seat backrest is one of the main components of a car [1]
For car seat backrest detection system, Visual Studio2017 was selected as the software development environment to develop machine vision
The car seat backrest classification and quality inspection system based on machine vision is proposed
Summary
Car seat backrest is one of the main components of a car [1]. With the progress of science and technology, the discussion on the safety of car parts is becoming more and more a subject of debate. It is very important to inspect parts of the car seats, such as back curtains and springs of car seat backrests. Machine vision technology can replace human eyes to detect whether car seat backrests are adequate [5, 6]. In 2019, Du et al [11] carried out an improved study on the detection method of the X-ray imaging of automobile casted aluminum parts; they proposed a defect detection system based on X-ray deep learning. An automatic detection system for the car seat backrest based on neural network was proposed. It combines the SURF (speeded up robust features) and neural network algorithm to classify A, B, and C types of car seat backrests. The classification of the car seat backrest and the function of qualified inspection for the problems of mismatching, misfitting, and missing of Q Company’s Class A car seat backrest were categorized
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