Şahin IŞIK, Göksu VATANSEVER, Yıldıran ANAGUN, Mehmet ÇELİKHAN, Kemal ÖZKAN

REAL TIME PEDESTRIAN ALERT SYSTEM FOR VEHICLES

In this study, we have developed a pre-collision alert system for vehicles in terms of detection pedestrians in road. The system is consisting from deep learning models and transfer learning methodologies. For this purpose, pre-trained convolutional models was considered to detect pedestrian and road. Finally, the segmented road mask and pedestrian mask was utilized to reveal the intersection of these two masks. The system generates an alert if the number of pixels is higher than predefined threshold value. By considering the visual results, the proposed system gives valuable detection results to avoid collision.

Keywords:

Deep Learning Models, Pedestrian Alert System, Road Detection,

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Eskişehir Technical University Journal of Science and Technology A - Applied Sciences and Engineering-Cover

ISSN: 2667-4211
Yayın Aralığı: Yılda 4 Sayı
Başlangıç: 2000
Yayıncı: Eskişehir Teknik Üniversitesi

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