Deep Learning Approach for Car Detection in UAV Imagery

Abstract

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.

Extraction of Pluvial Flood Relevant Volunteered Geographic Information (VGI) by Deep Learning from User Generated Texts and Photos

“…As described in DECAF [39], it is possible to use a pre-trained ConvNet as feature generator and apply classical machine learning such as Support Vector Machine (SVM) or logistic regression to train a model with good performance. Transfer learning is utilized, such as classification of satellite images [42], vehicles detection based on RGB images or LiDAR data [43,44], visual floor count determination [45] or visual localization [46]. Only recently, this approach was used for retrieving flooding relevant social media photos [27,28].…”

Car Detection from Low-Altitude UAV Imagery with the Faster R-CNN

“…Finally, SVM was used to classify the features into "car" and "non-car" objects. The proposed framework of [23] is similar to R-CNN [18], which was time-consuming when generating region proposals. Besides, different models should be trained for the three separate stages, which increases the complexity of [23].…”

“…Ammour et al [23] proposed a two-stage car detection method, including candidate regions extraction and classification stage. In the candidate regions extraction stage, the authors employed the mean-shift algorithm [24] to segment images.…”

Hybrid Spectral Unmixing: Using Artificial Neural Networks for Linear/Non-Linear Switching

“…Lyu et al [48] have demonstrated neural networks to be a good tool for unmixing using both linear and nonlinear methods simultaneously [52]. In [46], the use of artificial neural networks was reported to detect and count cars in Unmanned Areal Vehicle (UAV) images. Wu and Prasad [53] used neural networks for hyperspectral data classification, where a recurrent neural network was used to model the dependencies between different spectral bands and learn more discriminative features for hyperspectral data classification.…”

“…Deep learning has been extensively used in the literature for a range of different applications such as vehicle detection [45,46], investigated avalanche search and rescue operations with Unmanned Areal Vehicles (UAV), change detection [47,48]. In this scheme, high level features are learned from low level ones where the features derived can be formulated for pattern recognition classification [49].…”