头盔的不正确佩戴或缺失是摩托车驾驶中致命事故的重要促成因素。该数据集的目的是通过基于相机的分析来检测个人是否正确或错误地佩戴头盔。头盔数据集已经被策划,包括总共28,736张具有各种头盔类型的图像,包括全脸,半脸,模块化,和越野头盔,在正确和不正确的配置中。使用iPhone13和Mi10T手机拍摄,这些图像展示了不同的气候条件,从白天到晚上的场景。在图像采集之后,进行了预处理阶段以标准化数据集.这涉及重命名图像并将其尺寸调整为统一的768×576分辨率,之后,他们被组织到各自的文件夹。这个数据集的独特性在于它结合了不同的环境条件,全面的头盔类型,头盔方向的可变性,以及它作为一个庞大而平衡的数据集的地位,从而呈现现实世界场景的真实表示。数据集的实用程序扩展到各种机器学习任务,包括图像分类,物体检测,以及专门针对头盔识别的姿态估计。它的科学价值在于它有潜力推进与摩托车头盔使用相关的安全措施领域的研究和开发。
The improper wearing or absence of helmets represents a significant contributing factor to fatal accidents in motorcycle driving. This dataset serves the purpose of detecting whether individuals have correctly or incorrectly worn helmets through camera-based analysis. The Helmet dataset has been curated, comprising a total of 28,736 images featuring various helmet types, including Full-Face, Half-Face, Modular, and Off-Road Helmets, in both correct and incorrect configurations. Captured using an iPhone 13 and Mi10T mobile phones, the images exhibit diverse climatic conditions, ranging from daytime to night-time scenarios. Subsequent to image acquisition, a pre-processing phase was undertaken to standardize the dataset. This involved renaming the images and adjusting their dimensions to a uniform 768 × 576 resolution, after which they were organized into respective folders. The uniqueness of this dataset lies in its incorporation of diverse environmental conditions, comprehensive helmet types, variability in helmet orientations, and its status as a large and balanced dataset, thereby presenting a realistic representation of real-world scenarios. The dataset\'s utility extends to various machine learning tasks, including image classification, object detection, and pose estimation specifically geared towards helmet recognition. Its scientific value lies in its potential to advance research and development in the realm of safety measures associated with motorcycle helmet usage.