Comparative YOLO Training and Evaluation Outputs for Weapon Detection in Surveillance-Oriented Images

This dataset contains trained neural network weights, training configurations, and evaluation outputs for weapon detection in surveillance-oriented images using selected YOLO architectures. The work was created as a reproducible experimental output accompanying a bachelor’s thesis focused on the detection of handguns and knives using deep learning methods. The collection includes trained models based on YOLOv8, YOLOv10, YOLOv12, and YOLO26 in small, medium, and large variants. All models were trained under a unified experimental setup to enable direct comparison of their performance. The training procedure used a public annotated dataset containing two object classes: handgun and knife. The dataset includes:- trained model weights for all evaluated configurations,- training and execution scripts,- configuration files and parameter settings,- evaluation summaries and selected output metrics,- plots and visual reports generated during training and validation,- documentation supporting reproducibility of the experiments. The objective of this collection is to support reproducibility, benchmarking, and follow-up research in security-oriented computer vision, especially in tasks related to object detection in surveillance scenarios. The results show that medium-sized model variants achieved the most balanced performance under the given experimental conditions, while small and partially occluded objects remain challenging. This record does not republish the original third-party image dataset used for training unless explicitly permitted by its license. It provides derived research outputs only. Author contributions (CRediT): Lukáš Kopačka: Methodology, Software, Validation, Formal analysis, Writing – original draft. Kateřina Kolaříková: Data curation, Resources. Martin Haváček: Supervision, Project administration.本数据集包含针对监控场景图像的武器检测任务所训练的神经网络权重、训练配置，以及基于精选YOLO（You Only Look Once）架构得到的模型评估输出。本数据集为一篇聚焦于利用深度学习方法检测手枪与刀具的学士学位论文配套的可复现实验成果。 本数据集包含基于YOLOv8、YOLOv10、YOLOv12及YOLO26的小、中、大三种尺寸变体的训练后模型。所有模型均在统一的实验框架下开展训练，以实现性能的直接对标对比。训练过程使用了一份包含手枪、刀具两类目标的公开标注数据集。 本数据集涵盖以下内容： - 所有待评估配置对应的训练模型权重 - 训练与运行脚本 - 配置文件与参数设置 - 评估总结与选定的输出指标 - 训练及验证阶段生成的图表与可视化报告 - 支撑实验可复现性的配套文档 本数据集的研发目标是为安防导向的计算机视觉领域提供可复现性支持、性能基准测试参考，并助力后续相关研究，尤其适用于监控场景下的目标检测相关任务。实验结果表明，在本次设定的实验条件下，中等尺寸的模型变体取得了最均衡的性能表现，而小尺寸目标及部分遮挡目标仍是亟待解决的挑战。 除非获得原训练数据集的许可协议明确授权，本数据集未重新发布所用的第三方原始图像数据集，仅提供衍生研究成果。 作者贡献（CRediT贡献分类标准）：卢卡斯·科帕奇卡（Lukáš Kopačka）：方法学、软件开发、验证、形式分析、初稿撰写；卡特日娜·科拉里奇科娃（Kateřina Kolaříková）：数据整理、资源提供；马丁·哈瓦切克（Martin Haváček）：项目指导、项目管理。