Patil Kailas, Jadhav Rohini, Suryawanshi Yogesh, Chumchu Prawit, Khare Gaurav, Shinde Tanishk
Vishwakarma University, Pune, India.
Kasetsart University, Sriracha, Thailand.
Data Brief. 2024 Jul 31;56:110790. doi: 10.1016/j.dib.2024.110790. eCollection 2024 Oct.
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.
不规范佩戴头盔或不戴头盔是导致摩托车驾驶致命事故的一个重要因素。该数据集旨在通过基于摄像头的分析来检测个人是否正确佩戴了头盔。头盔数据集经过整理，共有28736张图像，包含各种类型的头盔，包括全脸盔、半脸盔、模块化头盔和越野头盔，既有正确佩戴的情况，也有错误佩戴的情况。这些图像是使用iPhone 13和小米10T手机拍摄的，呈现出从白天到夜间等不同的气候条件。图像采集后，进行了预处理阶段以标准化数据集。这包括重命名图像并将其尺寸调整为统一的768×576分辨率，之后将它们整理到各自的文件夹中。该数据集的独特之处在于它纳入了多样的环境条件、全面的头盔类型、头盔方向的变化，并且是一个大型且平衡的数据集，从而真实地呈现了现实世界的场景。该数据集的用途扩展到各种机器学习任务，包括图像分类、目标检测以及专门针对头盔识别的姿态估计。其科学价值在于它有可能推动与摩托车头盔使用相关的安全措施领域的研究与开发。
