Automated optical inspection(AOI)is a significant process in printed circuit board assembly(PCBA)production lines which aims to detect tiny defects in PCBAs.Existing AOI equipment has several deficiencies including lo...Automated optical inspection(AOI)is a significant process in printed circuit board assembly(PCBA)production lines which aims to detect tiny defects in PCBAs.Existing AOI equipment has several deficiencies including low throughput,large computation cost,high latency,and poor flexibility,which limits the efficiency of online PCBA inspection.In this paper,a novel PCBA defect detection method based on a lightweight deep convolution neural network is proposed.In this method,the semantic segmentation model is combined with a rule-based defect recognition algorithm to build up a defect detection frame-work.To improve the performance of the model,extensive real PCBA images are collected from production lines as datasets.Some optimization methods have been applied in the model according to production demand and enable integration in lightweight computing devices.Experiment results show that the production line using our method realizes a throughput more than three times higher than traditional methods.Our method can be integrated into a lightweight inference system and pro-mote the flexibility of AOI.The proposed method builds up a general paradigm and excellent example for model design and optimization oriented towards industrial requirements.展开更多
As the development of machine vision technology, the color line-scan system is widely applied in the on-line inspection. Due to the non-uniform gray scale and color distortion of the image acquired by the system, the ...As the development of machine vision technology, the color line-scan system is widely applied in the on-line inspection. Due to the non-uniform gray scale and color distortion of the image acquired by the system, the image correction is needed to reduce the problem of image processing and the stability system. Based on reasons mentioned above, a method that using polynomial fitting to correct the image is presented to solve the problem in this paper. The method has been used in the automatic optical inspection of PCB, and has been proved to be effective. So this method will have a potential application to the development of the color line-scan machine vision system.展开更多
基金supported in part by the IoT Intelligent Microsystem Center of Tsinghua University-China Mobile Joint Research Institute.
文摘Automated optical inspection(AOI)is a significant process in printed circuit board assembly(PCBA)production lines which aims to detect tiny defects in PCBAs.Existing AOI equipment has several deficiencies including low throughput,large computation cost,high latency,and poor flexibility,which limits the efficiency of online PCBA inspection.In this paper,a novel PCBA defect detection method based on a lightweight deep convolution neural network is proposed.In this method,the semantic segmentation model is combined with a rule-based defect recognition algorithm to build up a defect detection frame-work.To improve the performance of the model,extensive real PCBA images are collected from production lines as datasets.Some optimization methods have been applied in the model according to production demand and enable integration in lightweight computing devices.Experiment results show that the production line using our method realizes a throughput more than three times higher than traditional methods.Our method can be integrated into a lightweight inference system and pro-mote the flexibility of AOI.The proposed method builds up a general paradigm and excellent example for model design and optimization oriented towards industrial requirements.
文摘As the development of machine vision technology, the color line-scan system is widely applied in the on-line inspection. Due to the non-uniform gray scale and color distortion of the image acquired by the system, the image correction is needed to reduce the problem of image processing and the stability system. Based on reasons mentioned above, a method that using polynomial fitting to correct the image is presented to solve the problem in this paper. The method has been used in the automatic optical inspection of PCB, and has been proved to be effective. So this method will have a potential application to the development of the color line-scan machine vision system.
