Vehicle Detection in Aerial Images Based on Hyper Feature Map in Deep Convolutional Network

• Jiaquan Shen,
• Ningzhong Liu,
• Han Sun,
• Xiaoli Tao,
• Qiangyi Li,

Vol. 13, No. 4, April 29, 2019
10.3837/tiis.2019.04.014, Download Paper (Free):

• Car detection
• deep convolutional network
• hyper feature map
• small object detection
• feature fusion

Abstract

Vehicle detection based on aerial images is an interesting and challenging research topic. Most of the traditional vehicle detection methods are based on the sliding window search algorithm, but these methods are not sufficient for the extraction of object features, and accompanied with heavy computational costs. Recent studies have shown that convolutional neural network algorithm has made a significant progress in computer vision, especially Faster R-CNN. However, this algorithm mainly detects objects in natural scenes, it is not suitable for detecting small object in aerial view. In this paper, an accurate and effective vehicle detection algorithm based on Faster R-CNN is proposed. Our method fuse a hyperactive feature map network with Eltwise model and Concat model, which is more conducive to the extraction of small object features. Moreover, setting suitable anchor boxes based on the size of the object is used in our model, which also effectively improves the performance of the detection. We evaluate the detection performance of our method on the Munich dataset and our collected dataset, with improvements in accuracy and effectivity compared with other methods. Our model achieves 82.2% in recall rate and 90.2% accuracy rate on Munich dataset, which has increased by 2.5 and 1.3 percentage points respectively over the state-of-the-art methods.

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