摘要
The crime scene often reveals various types of wounds caused by weapons. It is the primary task of the first responders at the scene to determine the type of weapon based on the wound patterns. For newly graduated or inexperienced personnel, developing a mobile device capable of instantly identifying wounds and corresponding weapons would significantly enhance the efficiency and accuracy of crime scene investigations. This study utilizes deep neural networks to analyze the features of wound images and deploys the neural network on a portable Field Programmable Gate Array (FPGA) for hardware acceleration and offline recognition. Due to limited real wound photo data, we initially use simulated wounds made of resin clay as training data. By combining image processing techniques, we designed a neural network model with high accuracy and resolution. Experimental results demonstrate that this device has advantages such as lightweight, fast operation, accuracy, and offline capability, effectively overcoming environmental variables at the scene and providing objective analysis results to aid in the identification of weapons in crime scene investigations.
