Toward Fast and Accurate Vehicle Detection in Aerial Images Using Coupled Region-Based Convolutional Neural Networks

• Published in: IEEE journal of selected topics in applied earth observations and remote sensing Vol. 10; no. 8; pp. 3652 - 3664
• Main Authors: Deng, Zhipeng, Sun, Hao, Zhou, Shilin, Zhao, Juanping, Zou, Huanxin
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.08.2017; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Annotations; Artificial neural networks; Attribute learning; Automobiles; Computer applications; Computer vision; convolutional neural networks; Detection; Feature extraction; Feature maps; Frameworks; Image detection; Learning algorithms; Localization; Machine learning; Methods; Neural networks; Object detection; Object recognition; Proposals; State of the art; Training; Vehicle detection; vehicle proposal network; Vehicles
• ISSN: 1939-1404; 2151-1535
• DOI: 10.1109/JSTARS.2017.2694890

Vehicle detection in aerial images, being an interesting but challenging problem, plays an important role for a wide range of applications. Traditional methods are based on sliding-window search and handcrafted or shallow-learning-based features with heavy computational costs and limited representation power. Recently, deep learning algorithms, especially region-based convolutional neural networks (R-CNNs), have achieved state-of-the-art detection performance in computer vision. However, several challenges limit the applications of R-CNNs in vehicle detection from aerial images: 1) vehicles in large-scale aerial images are relatively small in size, and R-CNNs have poor localization performance with small objects; 2) R-CNNs are particularly designed for detecting the bounding box of the targets without extracting attributes; 3) manual annotation is generally expensive and the available manual annotation of vehicles for training R-CNNs are not sufficient in number. To address these problems, this paper proposes a fast and accurate vehicle detection framework. On one hand, to accurately extract vehicle-like targets, we developed an accurate-vehicle-proposal-network (AVPN) based on hyper feature map which combines hierarchical feature maps that are more accurate for small object detection. On the other hand, we propose a coupled R-CNN method, which combines an AVPN and a vehicle attribute learning network to extract the vehicle's location and attributes simultaneously. For original large-scale aerial images with limited manual annotations, we use cropped image blocks for training with data augmentation to avoid overfitting. Comprehensive evaluations on the public Munich vehicle dataset and the collected vehicle dataset demonstrate the accuracy and effectiveness of the proposed method.