A Comparative Study of Fusion Methods for Firearm Detection

In a previous study, we compared the performance of two well-known models, YOLOv8 and RT-DETR, for firearm detection. The results showed that YOLOv8 achieved superior performance, while RT-DETR also produced significant results. These findings suggested the potential to further improve detection by exploring result fusion methods. Unlike the original comparative analysis, this work investigates how integrating the outputs of multiple detectors can enhance both accuracy and robustness in firearm identification. The existing literature on result fusion, as explicitly applied to this field is scarce, leaving a promising line of research open. In this context, strategies such as averaging results, selecting the best detector, and time-weighted as well as real-time weighted methods are analyzed. The objective is to demonstrate that result fusion represents an effective method for enhancing the performance of object detection systems in complex scenarios. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2026.v17i2.1269Dimensions.Open Alex.