A Wireless Gunshot Recognition System Based on Tri-Axis Accelerometer and Lightweight Deep Learning

Gun violence and misuse pose great threat to the public safety. Real-time monitoring of gun usage and gunshot events are very promising for effective gun control. However, most available monitoring systems are installed in a fixed location instead of the guns, which greatly limits the flexibility and coverage. In this study, we propose a wireless gun monitoring and gunshot recognition system based on a low-cost triaxial acceleration sensor, which can monitor the gun in real time and accurately recognize gunshot events. Addressing the limited resources of the embedded systems, we further propose an efficient gunshot recognition algorithm EfficientNetTime that combines the lightweight neural network and knowledge distillation, so as to enable the deployment on embedded devices. First, a novel lightweight deep learning model is proposed as the basic model, which combines the advantages of 1-D convolution and depthwise separable convolution to effectively characterize the gunshot signal while decreasing the computing cost of convolution. Second, using the knowledge distillation, EfficientNetTime is used as the teacher model to generate a compressed student model that maintains accuracy and greatly reducing model size. Finally, the EfficientNetTime student model can be deployed on resource-limited embedded systems. The proposed method can automatically extract features for end-to-end recognition and is robust to temporal transformations of input signals. Using a publicly available gunshot data set, the proposed EfficientNetTime model is verified and compared against the state-of-the-art models. Experimental results demonstrate that the EfficientNetTime model surpasses other gunshot recognition methods in terms of the accuracy and model size.