Method for detecting surface defects of cloth based on wavelet neural network
A wavelet neural network and defect technology, applied in measuring devices, material analysis through optical means, instruments, etc., can solve problems such as difficult maintenance, high false detection rate, high cost, etc., to improve detection speed, shorten execution time, The effect of reducing missed detection and false detection rate
Inactive Publication Date: 2014-07-16
JIANGNAN UNIV +1
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Problems solved by technology
However, due to the continuous increase in the output of textiles and the improvement of the industrialization level of the production line, the traditional manual inspection method has been unable to keep up with the speed of automation development. Due to the disadvantages of low degree and high false detection rate, it is an urgent problem to be solved in the production process to quickly and accurately detect textile defects.
[0003] Faced with such a demand, some large-scale foreign enterprises have already had a certain scale of application in the industry. The main representative products include the IQ-TEX4 automatic online inspection system of the Israeli EVS company, the Cyclops automatic online fabric inspection system of the American BMS company, etc. , but the cost is high and maintenance is not easy, so it is not widely used in China
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[0019] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention will be further described in detail below in conjunction with specific embodiments and with reference to the accompanying drawings.
[0020] The basic purpose of the present invention is to check the surface flaws of the cloth, which is divided into offline training process and online detection process, and the hardware construction of the device is as follows: figure 1 As shown, the overall process of the algorithm is as follows figure 2 shown. In the offline training process, the wavelet network algorithm is used to process the flawless sample images, and the optimal parameter set is obtained by using the LM algorithm to iteratively optimize, and the corresponding odd symmetric Gabor filter bank and even symmetric Gabor filter bank are constructed. In the online detection process, the obtained filter bank is used to filter the image to be detec...
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The invention provides an online visual method for detecting surface defects of cloth. The detecting method is characterized in that the information such as width and direction of the surface textures of the cloth can be effectively extracted by combing Gabor filter and the wavelet neural network, the optimal solution of the same kind of cloth is obtained, then the Gabor filter is set up to perform online real-time detection, and the speed and the accuracy of the online detection can be ensured. A plurality of defects such as block defects and linear defects can be detected accurately and efficiently by using odd symmetry and even symmetry Gabor filters separately. In the circumstance of performing high-speed and real-time image acquisition by using a linear array camera, the detecting speed can be improved efficiently and the undetected rate and false detecting rate can be lowered.
Description
technical field [0001] The invention relates to a real-time visual detection method for cloth defects based on machine vision, specifically an image detection method for detecting and recording immediately the surface defects of cloth conveyed at high speed in an industrial site through a line array camera under a line array light source. Background technique [0002] In the industrial production process, with the continuous improvement of the technical level, the market's requirements for product quality have also been continuously improved. In the textile industry, the quality inspection requirements of cloth are becoming more and more strict with this development trend. However, due to the continuous increase in the output of textiles and the improvement of the industrialization level of the production line, the traditional manual inspection method has been unable to keep up with the speed of automation development. Due to the disadvantages of low degree and high false d...
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IPC IPC(8): G01N21/898
Inventor 白瑞林何薇吉峰李新
Owner JIANGNAN UNIV