Optimizing Multi‐Detector Fusion Through Data‐Balancing Techniques for Enhanced Weapon Detection Accuracy

ABSTRACT

Firearm and edged weapon detection in surveillance systems is critical for public safety. However, it faces significant challenges due to false positives. This study presents an automated data‐guided multi‐detector fusion framework with an innovative dataset balancing layer for model training. This balancing layer generates multiple datasets with carefully controlled image and instance distributions, reducing annotation bias and strengthening detector diversity. Several you only look once‐based detectors were trained independently on curated datasets. Their predictions are combined using weighted box fusion, which consistently outperforms other ensemble methods. The experimental results show a minimum false positive rate of 16%, the lowest among all tested datasets. This scalable and efficient approach presents a promising solution for weapon detection in safety‐critical environments. To ensure transparency and reproducibility, all datasets and source code related to this work are freely accessible at https://github.com/MuhammadIshtiaq/RQ2‐Dataset‐and‐Code . The repository provides detailed usage instructions, lists the required dependencies and describes the key algorithmic steps, allowing other researchers to reproduce the experiments and verify the findings.