Deep Learning Approach for Car Detection in UAV Imagery
Deep Learning Approach for Car Detection in UAV Imagery
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DOI：
10.3390/rs9040312
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发表时间：
2017-04-01
期刊：
影响因子：
5
通讯作者：
Zuair, Mansour
中科院分区：
文献类型：
--
作者：
Ammour, Nassim;Alhichri, Haikel;Zuair, Mansour
针对无人机图像中的车辆检测和计数问题，提出了一种自动检测和计数的方法。这是一项具有挑战性的任务，因为无人机图像的空间分辨率非常高(大约几厘米)，细节水平极高，需要适当的自动分析方法。我们提出的方法首先将输入图像分割成小的均匀区域，这些区域可以作为车辆检测的候选位置。接下来，在每个区域周围提取一个窗口，并使用深度学习从这些窗口中挖掘具有高度描述性的特征。我们使用已经在大量辅助数据上进行预训练的深度卷积神经网络(CNN)系统作为特征提取工具，并结合线性支持向量机(SVM)分类器将区域分类为汽车类和非汽车类。最后一步是微调过程，该过程执行形态扩张以平滑检测区域并填充任何空洞。此外，使用几个滑动矩形窗口对小的孤立区域进行进一步分析，以更准确地定位汽车并消除误报。为了评估我们的方法，我们在一组具有挑战性的真实无人机图像上进行了实验，这些图像在城市地区上空采集。实验结果表明，该方法在准确率和计算时间上都优于现有的方法。
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.