Tactile paving surface detection with deep learning methods - AKTAŞ, ABDULSAMET; DOĞAN, BUKET; DEMİR, ÖNDER (GAZI UNIV, FAC ENGINEERING ARCHITECTURE, 2020)
Image processing applications in real-time systems have become a popular topic in recent years. Deep learning methods, one of the sub-branches of artificial intelligence, and image processing algorithms used in the field of object detection from images can be used together. In this way, applications are developed in many areas such as autonomous cars, autonomous unmanned aerial vehicles, assist robot technologies, assistant technologies for disabled and elderly individuals. This study aims to detect the tactile paving surfaces with deep learning methods in order to design an assistive technology system that can be used by visually impaired individuals, autonomous vehicles and robots. Contrary to traditional image processing algorithms, deep learning methods and image processing algorithms are used together in this study. The YOLO-V3 model, which is one of the best methods of object detection, is combined with the DenseNet model to create the YOLOV3-Dense model. YOLO-V2, YOLO-V3 and YOLOV3-Dense models were trained on the Marmara Tactile Paving Surface (MDPY) dataset, which was created by the researchers and included 4580 images and their performances were compared with each other on the test dataset. It was observed that YOLOV3-Dense model is better than other models in detecting tactile paving surface with 89% F1-score, 92% mean average Precision(mAP) and 81% IoU values.
Keywords: Tactile paving; deep learning; convolutional neural networks; visually impaired persons; YOLO; IMAGE