# Confidence Aware SSD Ensemble with Weighted Boxes Fusion for Weapon Detection

arXiv: https://arxiv.org/abs/2509.23697 · HTML: https://mlsanity.com/paper/2509.23697/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 25 references that are in the map.
- First posted on arXiv 28 Sep 2025.

## Builds on (most cited first, 15 of 15)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [CountQA: How Well Do MLLMs Count in the Wild?](https://mlsanity.com/paper/2508.06585/) (2025)
- [GeoChain: Multimodal Chain-of-Thought for Geographic Reasoning](https://mlsanity.com/paper/2506.00785/) (2025)
- [HueManity: Probing Fine-Grained Visual Perception in MLLMs](https://mlsanity.com/paper/2506.03194/) (2025)
- [Video Vision Transformers for Violence Detection](https://mlsanity.com/paper/2209.03561/) (2022)
- [MaRVL-QA: A Benchmark for Mathematical Reasoning over Visual Landscapes](https://mlsanity.com/paper/2508.17180/) (2025)
- [Real-Time Weapon Detection Using YOLOv8 for Enhanced Safety](https://mlsanity.com/paper/2410.19862/) (2024)
- [AI Guide Dog: Egocentric Path Prediction on Smartphone](https://mlsanity.com/paper/2501.07957/) (2025)
- [SSIVD-Net: A Novel Salient Super Image Classification & Detection Technique for Weaponized Violence](https://mlsanity.com/paper/2207.12850/) (2022)
- [Detection of Malaria Vector Breeding Habitats using Topographic Models](https://mlsanity.com/paper/2011.13714/) (2020)
- [Novel Deep Learning Pipeline for Automatic Weapon Detection](https://mlsanity.com/paper/2309.16654/) (2023)
- [Gun Detection Using Combined Human Pose and Weapon Appearance](https://mlsanity.com/paper/2503.12215/) (2025)

## Abstract (arXiv)

The safety and security of public spaces is of vital importance, driving the need for sophisticated surveillance systems capable of accurately detecting weapons, which are often hampered by issues like partial occlusion, varying lighting, and cluttered backgrounds. While single-model detectors are advanced, they often lack robustness in these challenging conditions. This paper presents the hypothesis that ensemble of Single Shot Multibox Detector (SSD) models with diverse feature extraction backbones can significantly enhance detection robustness. To leverage diverse feature representations, individual SSD models were trained using a selection of backbone networks: VGG16, ResNet50, EfficientNet, and MobileNetV3. The study is conducted on a dataset consisting of images of three distinct weapon classes: guns, heavy weapons and knives. The predictions from these models are combined using the Weighted Boxes Fusion (WBF) method, an ensemble technique designed to optimize bounding box accuracy. Our key finding is that the fusion strategy is as critical as the ensemble's diversity, a WBF approach using a 'max' confidence scoring strategy achieved a mean Average Precision (mAP) of 0.838. This represents a 2.948% relative improvement over the best-performing single model and consistently outperforms other fusion heuristics. This research offers a robust approach to enhancing real-time weapon detection capabilities in surveillance applications by demonstrating that confidence-aware fusion is a key mechanism for improving accuracy metrics of ensembles.