Work

H-SGE: A hybrid model based on scene graph enrichment for automated Handgun detection in security surveillance

Year: 2025

Type: article

Abstract: Small handgun detection in CCTV surveillance suffers from high false positives and negatives due to limited distinguishing features. We propose H-SGE (Hybrid Scene Graph Enrichment), combining Generative Adversarial Networks (GANs), scene graph enrichment, and multiple YOLO variants (YOLOv5, YOLOv7, YOLO10, YOLO11) for enhanced detection. H-SGE employs a five-stage pipeline: (1) enriched scene graph generation, (2) GAN-based feature enhancement, (3) context-aware RoI selection, (4) multi-YOLO detection, and (5) output fusion. Evaluation on a handgun dataset demonstrates significant F1-score improvements: YOLOv5 from 58% to 80%, YOLOv7 from 56% to 82%, YOLO10 from 62% to 85%, and YOLO11 from 64% to 87%, achieving over 20% accuracy gains. Results show that combining contextual reasoning with visual augmentation and hybrid detection effectively addresses small object detection challenges in surveillance systems.

Source: Signal Image and Video Processing

DOI: 10.1007/s11760-025-04926-7

Authors: Nasreen Jawaid, Najma Imtiaz Ali, Imtiaz Ali Korejo, Imtiaz Ali Brohi, Nor Hafeizah Hassan

Institutions: University of Sindh, Universiti Teknikal Malaysia Melaka, Government College University Hyderabad

Language: English

Topic: Advanced Neural Network Applications

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $3,390

Funders: Universiti Teknikal Malaysia Melaka, Fakulti Teknologi Maklumat Dan Komunikasi, Universiti Teknikal Malaysia Melaka