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Related Experiment Video

Updated: Jul 16, 2025

Author Spotlight: Development of an Automated Camera-Based System for Real-Time Blast Overpressure Monitoring and TBI Risk Assessment in Military Training

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Published on: December 6, 2024

Conditioned Cooperative training for semi-supervised weapon detection.

Jose L Salazar González¹, Juan A Álvarez-García¹, Fernando J Rendón-Segador¹

¹Dpto. de Lenguajes y Sistemas Informáticos, Universidad de Sevilla, Spain.

Neural Networks : the Official Journal of the International Neural Network Society

|September 10, 2023

Summary

This summary is machine-generated.

New semi-supervised learning models enhance weapon detection using Closed Circuit Television (CCTV) footage. This approach significantly improves accuracy in identifying firearms, potentially reducing casualties from violent incidents.

Keywords:

Knowledge transfer Self-supervised learning Semi-supervised learning Supervised learning Weapon detection

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Published on: December 15, 2023

Area of Science:

Computer Science
Artificial Intelligence
Machine Learning

Background:

Violent assaults and mass shootings are persistent global issues with increasing victim counts annually.
Closed Circuit Television (CCTV) systems offer a potential avenue for crime reduction through advanced surveillance technologies.
Existing generic object detectors show promise but require specialized training for effective weapon identification.

Purpose of the Study:

To develop and evaluate a novel semi-supervised learning methodology for improved weapon detection using CCTV.
To introduce a new firearms image dataset for more effective model training and evaluation.
To compare the proposed method against existing supervised, semi-supervised, and self-supervised learning techniques.

Main Methods:

A semi-supervised learning framework utilizing conditioned cooperative student-teacher training.
Optimal pseudo-label generation through a novel confidence threshold search method.
Conditional knowledge transfer to enhance both student and teacher models.
Creation and utilization of a large-scale firearms image dataset (458,599 images) sourced from Instagram hashtags.

Main Results:

The proposed semi-supervised methodology significantly outperformed several state-of-the-art object detection models.
Improvements in Average Precision (AP) were observed, reaching up to +19.86% compared to YOLOv5m.
The approach demonstrated superior performance against Unbiased Teacher, DETReg, and UP-DETR models.
Evaluation highlighted the benefit of using a specialized firearms dataset over general datasets like ImageNet.

Conclusions:

The developed semi-supervised learning approach offers a substantial advancement in weapon detection capabilities.
The novel methodology and specialized dataset contribute to more accurate and reliable firearm identification in surveillance.
This technology holds potential for enhancing public safety and reducing gun violence through improved CCTV analysis.