AI-Powered Technologies Enable Purdue Northwest Researchers to Identify Gunfire
Researchers at Purdue University Northwest have developed an advanced gunshot detection system that utilizes AI and air quality sensors to improve safety in indoor environments, particularly schools and public buildings.
The system, led by Assistant Professor Wei Dai, consists of three key components: a physical air quality sensor, an AI-enhanced deep learning model, and an algorithm for self-calibration and self-testing.
The novel physical sensor is designed to detect increases in 10-micron air particles caused by shockwaves from gunshots. This approach allows for detection without compromising privacy as it monitors air quality rather than audio or video. Experimental tests confirmed that the sensor could detect particle increases from a gunshot fired from 141 feet away.
The AI algorithms integrated into the system are designed to distinguish gunshot events and reduce false positives, ensuring accurate detection. The self-calibration and self-testing features maintain the system's accuracy and reliability over time.
Large-scale testing of the system has been conducted in indoor public spaces to evaluate its performance and situational awareness enhancement, though specific results from mass deployment have not been detailed in the current information.
Wei Dai's team has developed three technologies to address the drawbacks in traditional gunshot detection systems. The first technology uses a physical sensor design to detect gunshot sounds with privacy protection. The second technology employs AI to identify gunshots while minimizing false alarms. The third technology provides the ability for self-calibrating/self-testing systems.
The AI technology runs on edge computers and server-based algorithms, using new deep-learning models trained with microphone sensors and air quality sensors. This technology improves sensor accuracy by detecting gunshots in occluded spaces and reduces privacy risks, as it does not rely on additional cameras.
The self-evaluating technology allows users to evaluate acoustic gunshot sensors at any time and in any location, using audio coding algorithms for testing one or multiple acoustic sensors without interrupting other people and routine operations.
The gunshot detection system developed by Wei Dai's team can be combined with other gunshot detection systems to reduce false alarms. The system, which can identify gunshots in a four-story building for less than $500 of hardware, aims to improve safety in school campuses and other public areas.
Related developments include AI-powered gunshot detectors by other universities, such as Eastern Michigan University, focusing on distinguishing real gunshots from false alarms and automating emergency notifications for faster police response in schools. Some comprehensive security solutions, like HALO devices, combine gunshot and air quality detection with other threat detection features.
Overall, this approach represents an innovative advancement in gunshot detection by leveraging AI and air particle sensing to create a privacy-conscious, accurate, and deployable safety system.
- The system developed by Wei Dai's team at Purdue University Northwest, which includes a physical air quality sensor, an AI-enhanced deep learning model, and an algorithm for self-calibration and self-testing, is a significant advancement in health-and-wellness, technology, and education-and-self-development, particularly for improving safety in indoor environments such as schools and public buildings.
- The advance gunshot detection system utilizes cybersecurity measures to address privacy concerns, as it monitors air quality rather than audio or video data, making it a promising solution for data-and-cloud-computing and personal-growth enthusiasts concerned about privacy and security.
- The application of AI and air particle sensing in gunshot detection systems offers opportunities for further research and development in various fields, including science and technology, potentially leading to the creation of more innovative and effective safety systems.
- As more universities develop AI-powered gunshot detection systems, competition in this area may lead to advancements in technology that promote learning and self-improvement through innovation, ultimately contributing to overall societal growth and progress.
