Photovoltaic glass edge defect detection based on improved SqueezeNet.

With the global energy shortage, countries all over the world are vigorously developing new energy sources, and photovoltaic glass, as an important raw material for photovoltaic power generation, puts forward higher requirements for its output and quality. In order to solve the problems of low efficiency, susceptibility to interference by human factors, and low detection accuracy during the detection of photovoltaic glass edge defects by traditional manual methods, this paper proposes an automatic detection method of photovoltaic glass edge defects based on machine vision technology. Firstly, a machine vision defect detection system is designed to meet the needs of photovoltaic glass edge defect detection, includes high-contrast imaging solutions with a combination of multiple light sources for illumination and an automated transmission scheme with high stability, and an image dataset is established; Secondly, according to the characteristics of defect detection process with many interfering factors and high requirements for detection efficiency and accuracy, a deep learning defect detection method is proposed to improve the SqueezeNet model, which incorporates dense residual units into the two-part Fire Module of the classical SqueezeNet network model to extract the important feature information of the glass edge image, and effectively avoids the influence of interfering factors, such as water droplets, on the detection of defects. Finally, the improved SqueezeNet network algorithm is applied in the machine vision glass edge defect detection system designed in this paper. The experimental results show that the average leakage rate of the photovoltaic glass edge defect detection method proposed in this paper is 0.0064%, the misdetection detection rate is 0.0075%, and the average detection time is 2.715 s, and can meet the requirements of the automated production of photovoltaic glass. [ABSTRACT FROM AUTHOR]