Publisher
University of Tennessee at Chattanooga
Place of Publication
Chattanooga (Tenn.)
Abstract
Gun violence is a growing public safety concern, highlighting the need for fast and accurate gunshot detection systems (GDS) to support real-time law enforcement response. This review explores the integration of deep learning (DL), artificial intelligence (AI), and the Internet of Things (IoT) in enhancing GDS performance. It summarizes recent advances ranging from physics-based acoustic modeling to data-driven methods like CNNs, RNNs, and hybrid neural networks, along with edge-based IoT frameworks for low-latency alerts. The review also examines challenges such as differentiating gunshots from similar sounds, ensuring robustness across environments, and addressing surveillance-related privacy concerns. Case studies from urban, indoor, and wildlife deployments illustrate real-world applications. By synthesizing current methods and deployment strategies, this work identifies emerging research opportunities to guide the development of scalable, ethical, and real-time safety solutions.
YouYube Video Link: https://youtu.be/NNXk5iZ4idA
Document Type
posters
Language
English
Rights
http://rightsstatements.org/vocab/InC/1.0/
License
http://creativecommons.org/licenses/by/4.0/
Recommended Citation
K Kumarasamy, Vijayalakshmi; Anandhan, Anitha; Liang, Yu; and Wu, Dalei, "Advancements in Gunshot Detection: AI, IoT, and Deep Learning for Real-Time Response". ReSEARCH Dialogues Conference proceedings. https://scholar.utc.edu/research-dialogues/2025/posters/15.
Advancements in Gunshot Detection: AI, IoT, and Deep Learning for Real-Time Response
Gun violence is a growing public safety concern, highlighting the need for fast and accurate gunshot detection systems (GDS) to support real-time law enforcement response. This review explores the integration of deep learning (DL), artificial intelligence (AI), and the Internet of Things (IoT) in enhancing GDS performance. It summarizes recent advances ranging from physics-based acoustic modeling to data-driven methods like CNNs, RNNs, and hybrid neural networks, along with edge-based IoT frameworks for low-latency alerts. The review also examines challenges such as differentiating gunshots from similar sounds, ensuring robustness across environments, and addressing surveillance-related privacy concerns. Case studies from urban, indoor, and wildlife deployments illustrate real-world applications. By synthesizing current methods and deployment strategies, this work identifies emerging research opportunities to guide the development of scalable, ethical, and real-time safety solutions.
YouYube Video Link: https://youtu.be/NNXk5iZ4idA