The field-of-view defect of UV image intensifier is acknowledged as a crucial element impacting the imaging performance of such device.The data enhancement procedures are used to address the issues of few field-of-view defect samples and the significant variances of images in field-of-view.A feature pyramid network(FPN)is added to the single shot multibox detector(SSD)algorithm for the successful detection and fusion of multiscale features.A convolutional block attention module(CBAM)is also introduced to improve the network's focus on small defect targets and minimize noise interference.The experimental results show that,on the self-constructed dataset,the feature pyramid network-convolutional block attention module-single shot multibox detector(FPN-CBAM-SSD)algorithm outperforms the SSD algorithm significantly in the actual detection of field-of-view defects.For 5 categories of defects including bright spots,dark patches,striped defects,bright patches,and dark spots,the average detection accuracy is improved by 19.76％,22.84％,29.56％,34.55％,and 38.14％,respectively.FPN-CBAM-SSD algorithm is capable of meeting the practical application requirements and adapting to more complex field-of-view conditions,making it an effective method for detecting field-of-view defects in ultraviolet image intensifiers.
维普
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