Automatic Detection of Pedestrian Crosswalk with Faster R-CNN and YOLOv7

• Published in: Buildings (Basel) 2023-04, Vol.13 (4), p.1070
• Main Authors: Kaya, Ömer, Çodur, Muhammed Yasin, Mustafaraj, Enea
• Format: Article
• Language: English
• Subjects: Accident prevention; Accidents; Accuracy; Addictions; Automobiles; autonomization; Autonomous vehicles; Autonomy; Blindness; Datasets; Deep learning; Developing countries; Driverless cars; Equipment and supplies; image processing; Incompatibility; intelligent transportation systems; LDCs; Model accuracy; Motor vehicles; object detection; Pedestrian crossings; Pedestrians; People with disabilities; Planning; Roads; Roads & highways; Semantics; Traffic accidents & safety; Traffic flow; traffic safety; Unmanned aerial vehicles; Visual impairment; Warning systems
• ISSN: 2075-5309; 2075-5309
• DOI: 10.3390/buildings13041070

Autonomous vehicles have gained popularity in recent years, but they are still not compatible with other vulnerable components of the traffic system, including pedestrians, bicyclists, motorcyclists, and occupants of smaller vehicles such as passenger cars. This incompatibility leads to reduced system performance and undermines traffic safety and comfort. To address this issue, the authors considered pedestrian crosswalks where vehicles, pedestrians, and micro-mobility vehicles collide at right angles in an urban road network. These road sections are areas where vulnerable people encounter vehicles perpendicularly. In order to prevent accidents in these areas, it is planned to introduce a warning system for vehicles and pedestrians. This procedure consists of multi-stage activities by sending warnings to drivers, disabled individuals, and pedestrians with phone addiction simultaneously. This collective autonomy is expected to reduce the number of accidents drastically. The aim of this paper is the automatic detection of a pedestrian crosswalk in an urban road network, designed from both pedestrian and vehicle perspectives. Faster R-CNN (R101-FPN and X101-FPN) and YOLOv7 network models were used in the analytical process of a dataset collected by the authors. Based on the detection performance comparison between both models, YOLOv7 accuracy was 98.6%, while the accuracy for Faster R-CNN was 98.29%. For the detection of different types of pedestrian crossings, YOLOv7 gave better prediction results than Faster R-CNN, although quite similar results were obtained.