Pedestrian and Vehicles Detection with ResNet in Aerial Images
Yazarlar (4)
Dr. Öğr. Üyesi Enes CENGİZ Afyon Kocatepe Üniversitesi, Türkiye
Prof. Dr. Cemal Yilmaz Gazi Üniversitesi, Türkiye
Prof. Dr. Hamdi Tolga Kahraman Karadeniz Teknik Üniversitesi, Türkiye
Dr. Öğr. Üyesi Fatih Bayram Afyon Kocatepe Üniversitesi, Türkiye
| Bildiri Türü | Tebliğ/Bildiri | Bildiri Dili | Ingilizce |
| Bildiri Alt Türü | Tam Metin Olarak Yayınlanan Tebliğ (Uluslararası Kongre/Sempozyum) | ||
| Bildiri Niteliği | Alanında Hakemli Uluslararası Kongre/Sempozyum | ||
| DOI Numarası | 10.36287/setsci.4.6.107 | ||
| Kongre Adı | 4th International Symposium on Innovative Approaches in Engineering and Natural Sciences Proceedings | ||
| Kongre Tarihi | 22-11-2019 / 24-11-2019 | ||
| Basıldığı Ülke | Türkiye | Basıldığı Şehir | |
| Bildiri Linki | http://www.set-science.com/?go=d1001a2417e2b87d5b7c53e16c5e1675conf_id=84paper_id=107 | ||
| UAK Araştırma Alanları |
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| Özet |
| In today's applications, a significant increase in the use of deep learning algorithms is noticeable. The convolution neural network (CNN) of deep learning has been used frequently recently, especially for the successful discrimination of people and vehicles from other objects. Especially with the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) in 2012, the use of CNNs has become widespread. With the development of technology and traditional image processing techniques, the proses of image processing has been considerably reduced, furthermore, the success rate has increased dramatically. Object detection can be difficult due to the low resolution of objects in aerial images. In this study, a system which automatically recognizes human and different types of vehicles (cars, bicycles, motorcycles) from aerial images taken with drone has been developed. In the system, Residual Networks (ResNet) model, which is the first in the ImageNet competition of the CNN been one of the deep learning techniques, is used. Google Colaboratory with Nvidia Tesla K80 GPU support is used for successful and fast training and testing of the system. In the developed system, results are explained according to different threshold values of the objects detected from the images applied to the input. |
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BM Sürdürülebilir Kalkınma Amaçları
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| Google Scholar | 8 |