(IRTSD-Datasetv1)-Indian Road Traffic Sign Detection dataset

The advancement of machine and deep learning methods in traffic sign detection is critical for improving road safety and developing intelligent transportation systems. However, the scarcity of a comprehensive and publicly available dataset on Indian traffic has been a significant challenge for researchers in this field. To reduce this gap, we introduced the Indian Road Traffic Sign Detection dataset (IRTSD-Datasetv1), which captures real-world images across diverse conditions. Our dataset consists of 5141 images spanning 37 traffic sign classes, collected from over 90 cities in India, with varying distances and lighting conditions, using mobile phones. To demonstrate the effectiveness of our dataset, we evaluated it using YOLOv8, YOLOv10, and RT-DETR algorithms, achieving a mean average precision (mAP) of up to 98.25%. We believe that our dataset provides a solid foundation for future research in traffic sign detection or any computer vision task, contributing to significant advancements in this field.

交通标志检测领域的机器学习与深度学习方法进展，对于提升道路交通安全、发展智能交通系统至关重要。然而，当前缺乏覆盖全面且可公开获取的印度交通场景数据集，这已成为该领域研究者面临的重大挑战。为填补这一研究空白，我们推出了印度道路交通标志检测数据集（Indian Road Traffic Sign Detection Dataset, IRTSD-Datasetv1），该数据集采集了多样化场景下的实景图像。本数据集包含5141张图像，涵盖37类交通标志，采集自印度90余座城市，采用移动设备拍摄，覆盖了不同拍摄距离与光照条件等多种变量。为验证本数据集的有效性，我们使用YOLOv8、YOLOv10以及RT-DETR算法对其开展评估，最终实现了最高达98.25%的平均精度均值（mean average precision, mAP）。我们认为，本数据集可为未来交通标志检测或任意计算机视觉任务的研究提供坚实基础，助力该领域取得重大进展。

搜集汇总

背景与挑战

背景概述

IRTSD-Datasetv1是一个包含5141张印度道路交通标志图像的数据集，涵盖37个类别，采集自90多个城市的不同条件。该数据集旨在支持交通标志检测研究，填补了印度相关公开数据集的空白。