Deep-Transfer-Learning-Based Intelligent Gunshot Detection and Firearm Recognition Using Tri-Axial Acceleration

Reliable identification of gunshot events is crucial for reducing gun violence and enhancing public safety. However, current gunshot detection and recognition methods are still affected by complex shooting scenarios, various nongunshot events, diverse firearm types, and scarce gunshot datasets. To address these issues, based on triaxial acceleration of guns, a novel general deep transfer learning approach is proposed for gunshot detection and recognition, which combines a temporal deep learning model with transfer learning and automated machine learning (AutoML) to improve the accuracy, reliability and generalization performance. First, a new gunshot recognition model named as MobileNetTime is proposed for the two-class gunshot event detection, three-class coarse firearm recognition, and 15-class fine firearm recognition, which utilizes 1-D convolution and inverted residual modules to autonomously extract higher-level features from the time series acceleration data. Second, considering the impact of nongunshot events, the AutoML is employed for model fine tuning, to transfer the pretrained MobileNetTime from the handgun to various firearm types. In addition, we propose a low-power versatile gunshot recognition system framework employing a triaxial accelerometer for both of wrist-worn and gun-embedded scenarios, which adopts a two-stage wake-up mechanism that selectively monitors gunshot events using temporal and spectral energy features. The experimental results on the two gunshot datasets DGUWA and GRD show that the proposed model can achieve up to 100% accuracy on the DGUWA dataset and 98.98% accuracy on the GRD dataset for the two-class gunshot detection. Moreover, the proposed deep transfer learning approach achieves a 98.98% accuracy for 16-class firearm classification, which is 6.21% higher than the model without transfer learning.