Deep Learning Approach for Car Detection in UAV Imagery

• Published in: Remote sensing (Basel, Switzerland) Vol. 9; no. 4; p. 312
• Main Authors: Ammour, Nassim, Alhichri, Haikel, Bazi, Yakoub, Benjdira, Bilel, Alajlan, Naif, Zuair, Mansour
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-04-2017
• Subjects: Automobiles; Aversion learning; car counting; Computer applications; convolutional neural networks (CNNs); Deep learning; Feature extraction; Image classification; Image detection; mean-shift segmentation; Neural networks; Serial learning; Spatial discrimination; Spatial resolution; Support vector machines; support vector machines (SVM); UAV imagery; Unmanned aerial vehicles; Urban areas
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs9040312

This paper presents an automatic solution to the problem of detecting and counting cars in unmanned aerial vehicle (UAV) images. This is a challenging task given the very high spatial resolution of UAV images (on the order of a few centimetres) and the extremely high level of detail, which require suitable automatic analysis methods. Our proposed method begins by segmenting the input image into small homogeneous regions, which can be used as candidate locations for car detection. Next, a window is extracted around each region, and deep learning is used to mine highly descriptive features from these windows. We use a deep convolutional neural network (CNN) system that is already pre-trained on huge auxiliary data as a feature extraction tool, combined with a linear support vector machine (SVM) classifier to classify regions into “car” and “no-car” classes. The final step is devoted to a fine-tuning procedure which performs morphological dilation to smooth the detected regions and fill any holes. In addition, small isolated regions are analysed further using a few sliding rectangular windows to locate cars more accurately and remove false positives. To evaluate our method, experiments were conducted on a challenging set of real UAV images acquired over an urban area. The experimental results have proven that the proposed method outperforms the state-of-the-art methods, both in terms of accuracy and computational time.