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<p>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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月6日

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  • Nasreen Jawaid,
  • Najma Imtiaz Ali,
  • Imtiaz Ali Korejo,
  • Imtiaz Ali Brohi,
  • Nor Hafeizah Hassan

摘要

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.