CeyMo: See More on Roads -- A Novel Benchmark Dataset for Road Marking Detection

TL;DR

CeyMo introduces a comprehensive, high-resolution benchmark dataset for road marking detection, including diverse annotations and evaluation tools, to advance research in autonomous driving and traffic safety.

Contribution

The paper presents a new large-scale, high-resolution dataset with diverse annotations and an evaluation script, addressing limitations of existing datasets for road marking detection.

Findings

Evaluation of instance segmentation and object detection approaches

Baseline speed and accuracy metrics provided

Dataset covers various traffic, lighting, and weather conditions

Abstract

In this paper, we introduce a novel road marking benchmark dataset for road marking detection, addressing the limitations in the existing publicly available datasets such as lack of challenging scenarios, prominence given to lane markings, unavailability of an evaluation script, lack of annotation formats and lower resolutions. Our dataset consists of 2887 total images with 4706 road marking instances belonging to 11 classes. The images have a high resolution of 1920 x 1080 and capture a wide range of traffic, lighting and weather conditions. We provide road marking annotations in polygons, bounding boxes and pixel-level segmentation masks to facilitate a diverse range of road marking detection algorithms. The evaluation metrics and the evaluation script we provide, will further promote direct comparison of novel approaches for road marking detection with existing methods. Furthermore, we evaluate the effectiveness of using both instance segmentation and object detection based approaches for the road marking detection task. Speed and accuracy scores for two instance segmentation models and two object detector models are provided as a performance baseline for our benchmark dataset. The dataset and the evaluation script is publicly available at https://github.com/oshadajay/CeyMo.