本文探讨了一种深度学习方法来评估墨盒外壳图像中圆形分隔符的位置。这些分隔符定义了两个感兴趣区域(ROI),对应于臀位和撞针的印象,并手动或通过图像处理算法放置。这种定位对枪械识别的图像匹配算法的性能有很大的影响,自动评估方法对任何计算机化系统都是有益的。我们的贡献在于优化和训练来自墨盒情况的数字图像的U-Net分割模型,打算自动定位ROI。对于实验,我们使用了不同9MM枪支发射的1195个弹壳样本的高分辨率2D图像。我们的结果表明,分割模型,在增强数据集上训练,表现出95.6%的IoU(交汇处)和99.3%的DC(骰子系数)的性能,对于臀位面部图像的损失为0.014;以及95.9%的IoU和99.5%的DC的性能,对于撞针图像的损失为0.011。我们观察到,与地面真实掩模上的完美圆相比,预测圆的自然形状降低了分割模型的性能,这表明我们的方法提供了对真实ROI形状的更准确分割。在实践中,我们相信这些结果可能对枪支识别有用。在今后的工作中,预测可用于评估数据库中标本的分隔符质量,或者他们可以确定墨盒外壳图像上的感兴趣区域。
This paper explores a deep-learning approach to evaluate the position of circular delimiters in cartridge case images. These delimiters define two regions of interest (ROI), corresponding to the breech face and the firing pin impressions, and are placed manually or by an image-processing algorithm. This positioning bears a significant impact on the performance of the image-matching algorithms for firearm identification, and an automated evaluation method would be beneficial to any computerized system. Our contribution consists in optimizing and training U-Net segmentation models from digital images of cartridge cases, intending to locate ROIs automatically. For the experiments, we used high-resolution 2D images from 1195 samples of cartridge cases fired by different 9MM firearms. Our results show that the segmentation models, trained on augmented data sets, exhibit a performance of 95.6% IoU (Intersection over Union) and 99.3% DC (Dice Coefficient) with a loss of 0.014 for the breech face images; and a performance of 95.9% IoU and 99.5% DC with a loss of 0.011 for the firing pin images. We observed that the natural shapes of predicted circles reduce the performance of segmentation models compared with perfect circles on ground truth masks suggesting that our method provide a more accurate segmentation of the real ROI shape. In practice, we believe that these results could be useful for firearms identification. In future work, the predictions may be used to evaluate the quality of delimiters on specimens in a database, or they could determine the region of interest on a cartridge case image.