AI-Based Detection and Classification of Horizontal Road Markings in Digital Images Dedicated to Driver Assistance Systems
Source abstract
Abstract
Horizontal road markings are crucial for safe driving and for the operation of advanced driver-assistance systems (ADAS), but they have been investigated less thoroughly than vertical signs or lane boundaries. This paper focuses on the detection and classification of horizontal road markings in digital images using modern deep learning techniques. Three YOLO models (YOLOv7, YOLOv8n, YOLOv9t) were trained and tested on a new dataset comprising 6250 images with 13,360 annotated horizontal road-marking objects across nine classes collected on Polish roads in sunny and cloudy conditions. The dataset covers nine classes of markings recorded on urban streets, rural roads and highways. It includes many difficult cases: small markings visible only from long distance or side entry roads, and markings with different levels of wear, from new and bright to faded, dirty or partially erased. YOLOv7 achieved Precision = 0.95, Recall = 0.91 and mAP@0.5 = 0.98. YOLOv8n and YOLOv9t obtained lower Recall but higher mAP@0.5:0.95 (>0.77). The results confirm that YOLO-based detectors can handle horizontal road markings under varied road conditions and degrees of visibility, and the dataset with baseline results may serve as a reference for further studies in intelligent transport systems.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced Neural Network Applications · Autonomous Vehicle Technology and Safety · Infrastructure Maintenance and Monitoring
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Computer Science, Faculty of Computer Science and Artificial Intelligence, Czestochowa University of Technology, Dabrowskiego 69, 42-201 Czestochowa, Poland
References (32)
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