Firearm violence is one of the leading causes of death in many countries around the world, including Thailand. This work proposes a fast and accurate automated method to classify firearm brands from bullet markings. Specifically, a panoramic image of a bullet collected from a crime scene was captured using a developed mobile phone application and custom-built portable hardware. The top three state-of-the-art CNNs pretrained on ImageNet-DenseNet121, ResNet50, and Xception-were further trained on the same training set, which was composed of 718 bullets collected from eight different firearm brands-Beretta, Browning, CZ, Glock, Norinco, Ruger, Sig Sauer, and Smith & Wesson-using a five-fold cross validation technique. DenseNet121 provided the highest AUC of 0.99 for CZ classification (the most common registered firearm brand in Thailand) and the highest average AUC for the eight firearm brands (0.9780 ± 0.0130 SD), which was significantly higher than those of ResNet50 and Xception. In addition, there were no interaction effects between the CNN model and firearm brand on AUC. DenseNet121, which had the highest AUC, was evaluated on the test set (72 bullets), and the results showed that the Beretta and CZ classifications had the lowest accuracy (91.18%), followed by the Browning and Norinco classifications (96.88%), whereas the Glock, Ruger, Sig Sauer, and Smith & Wesson classifications had the highest accuracy (98.41%). These results suggest that the developed mobile phone application based on a deep learning algorithm and the custom-built portable hardware have promising potential for use at crime scenes to classify firearms from bullet markings. By narrowing down the list of suspects, this convenient approach can potentially accelerate bullet identification processes for many forensic science examiners. INDEX TERMS Forensic science, automated firearm classification, 9 mm bullet marking, densely connected convolutional network.
