A Fast Identification Method of Gunshot Types Based on Knowledge Distillation

Abstract

To reduce the large size of a gunshot recognition network model and to improve the insufficient real-time detection in urban combat, this paper proposes a fast gunshot type recognition method based on knowledge distillation. First, the muzzle blast and the shock wave generated by the gunshot are preprocessed, and the quality of the gunshot recognition dataset is improved using Log-Mel spectrum corresponding to these two signals. Second, a teacher network is constructed using 10 two-dimensional residual modules, and a student network is designed using depth wise separable convolution. Third, the lightweight student network is made to learn the gunshot features under the guidance of the pre-trained large-scale teacher network. Finally, the network’s accuracy, model size, and recognition time are tested using the AudioSet dataset and the NIJ Grant 2016-DN-BX-0183 gunshot dataset. The findings demonstrate that the proposed algorithm achieved 95.6% and 83.5% accuracy on the two datasets, the speed was 0.5 s faster, and the model size was reduced to 2.5 MB. The proposed method is of good practical value in the field of gunshot recognition.