Multimodal Weapon Detection and Forecasting Application for Visible and Thermal-Based Concealed Threats using RGB and Thermal Data

Panbarasan D; Mukundan CS; Naveen Kumar R; Ashok Kumar S

doi:10.36948/ijfmr.2026.v08i03.78333

Multimodal Weapon Detection and Forecasting Application for Visible and Thermal-Based Concealed Threats using RGB and Thermal Data

Author(s)	Mr. Panbarasan D, Mr. Mukundan CS, Mr. Naveen Kumar R, Prof. Ashok Kumar S
Country	India
Abstract	With growing needs for securing public and private spaces, a new trend of introducing better security systems has emerged. In particular, it gave rise to smart surveillance systems capable of detecting any dangers in real-time. For that reason, we propose the creation of a multimodal weapon detection and forecasting system. By using RGB (visible light) and thermal imagery, it will be able to perform detection tasks in challenging environments, like those of low lighting and/or partial visibility. To detect weapons in real-time, two models using the YOLO (You Only Look Once) algorithm were implemented. One of them works with RGB data, and the other with thermal imagery. As a means to compensate for the disadvantages of using only one type of data, their outputs are compared, with the predictions being merged at the decision level. The prediction with the highest confidence score becomes final for the current frame. As far as implementation goes, the system can process both live camera feed and image uploads from users. Temporal filtering is employed to eliminate false positives, which occur through comparing several consecutive detections. Upon detecting the presence of a weapon, the system sends out a signal that involves a sound alarm and an email notification containing the corresponding picture. In addition to that, there is a module that allows the user to perform analytics and forecasting. Using historical data, this algorithm would identify patterns, calculate potential future threats, and determine peak activity periods. In this way, the detection system will transition into a forecasting system. Thus, it can be concluded that a multimodal detection system that utilizes RGB and thermal imagery, along with simple analytics, can be quite efficient. General Terms: Artificial Intelligence, Computer Vision, Machine Learning, Image Processing, Surveillance Systems, Security Systems Keywords: Multimodal Weapon Detection, RGB and Thermal Imaging, YOLO, Computer Vision, Real-Time Surveillance, Decision-Level Fusion, Predictive Analytics
Keywords	Multimodal Weapon Detection, RGB and Thermal Imaging, YOLO, Computer Vision, Real-Time Surveillance, Decision-Level Fusion, Predictive Analytics
Field	Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In	Volume 8, Issue 3, May-June 2026
Published On	2026-05-14
DOI	https://doi.org/10.36948/ijfmr.2026.v08i03.78333

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Multimodal Weapon Detection and Forecasting Application for Visible and Thermal-Based Concealed Threats using RGB and Thermal Data

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