Image-Based Threat Detection and Explainability Investigation Using Incremental Learning and Grad-CAM with YOLOv8

Abstract

Real-world threat detection systems face critical challenges in adapting to evolving operational conditions while providing transparent decision making. Traditional deep learning models suffer from catastrophic forgetting during continual learning and lack interpretability in security-critical deployments. This study proposes a distributed edge–cloud framework integrating YOLOv8 object detection with incremental learning and Gradient-weighted Class Activation Mapping (Grad-CAM) for adaptive, interpretable threat detection. The framework employs distributed edge agents for inference on unlabeled surveillance data, with a central server validating detections through class verification and localization quality assessment (IoU ≥ 0.5). A lightweight YOLOv8-nano model (3.2 M parameters) was incrementally trained over five rounds using sequential fine tuning with weight inheritance, progressively incorporating verified samples from an unlabeled pool. Experiments on a 5064 image weapon detection dataset (pistol and knife classes) demonstrated substantial improvements: F1-score increased from 0.45 to 0.83, [email protected] improved from 0.518 to 0.886 and minority class F1-score rose 196% without explicit resampling. Incremental learning achieved a 74% training time reduction compared to one-shot training while maintaining competitive accuracy. Grad-CAM analysis revealed progressive attention refinement quantified through the proposed Heatmap Focus Score, reaching 92.5% and exceeding one-shot-trained models. The framework provides a scalable, memory-efficient solution for continual threat detection with superior interpretability in dynamic security environments. The integration of Grad-CAM visualizations with detection outputs enables operator accountability by establishing auditable decision records in deployed systems.