Road Mark Detection

In recent years, advancements in computer vision and deep learning have significantly transformed object detection, enabling a wide range of applications across various domains. One crucial application is road safety. Accurate and efficient object detection plays a vital role in preventing accidents and improving traffic management. To address the unique challenges in this domain, researchers and developers have created specialized datasets tailored specifically for road-related object detection tasks.

The ROAD MARK Dataset is a comprehensive and valuable resource for training and evaluating object detection models. Specifically, it is a curated collection of annotated images and videos that focus on detecting and classifying road markings and related objects. Additionally, it encompasses a diverse range of scenarios encountered on roads, including highways, urban streets, and rural areas. This diversity makes it highly representative of real-world conditions.

To begin with, the ROAD MARK Dataset includes various types of road environments. Moreover, its annotations are meticulously designed to help models understand different road markings.

Moreover, the dataset includes various types of road markings, such as lane lines, arrows, pedestrian crossings, speed limit signs, and other relevant objects like traffic lights and barriers. Additionally, this dataset contains 2892 samples, which are correctly divided into three parts: Train, Valid, and Test.