A Deep Learning Based Explosion Point Image Recognition and Processing Method
To improve the testing accuracy of the projectile's near-impact point and overcome the recognition deficien-cies caused by noise interference and environmental factors in traditional explosion point images,a deep learning-based explosion point image recognition and processing method was proposed in this paper.Based on multi-sequence explosion point images captured by a high-speed camera,the method utilized GoogLeNet classification network to extract explo-sion point information at the moment of explosion,the improvement of the U-Net network for segmenting explosion point images were studied,which focused on modeling the main feature extraction and optimization loss function of ex-plosion point images,then explosion point image datasets were trained and tested.The Canny edge detection algorithm was adopted to extract the edge of explosion point images,the least squares method was applied for contour fitting,and pixel coordinates of explosion points were solved.By combining camera's spatial geometry,spatial coordinates of the ex-plosion point were obtained.Experimental results demonstrated that the improved U-Net network achieved a segmenta-tion precision with a PA value of 94.2%,an MPA value of 97.6%,and an MIOU value of 84.8%,which was higher than that of original U-Net network.The proposed method could provide technical support for obtaining the location of explosion points.
U-net networkimage recognitionimage segmentationloss function
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