Abstract
Vehicles in aerial images are generally with small sizes and unbalanced number of samples, which leads to the poor performances of the existing vehicle detection algorithms. Therefore, an oriented vehicle detection framework based on improved Faster RCNN is proposed for aerial images. First of all, we propose an oversampling and stitching data augmentation method to decrease the negative effect of category imbalance in the training dataset and construct a new dataset with balanced number of samples. Then considering that the pooling operation may loss the discriminative ability of features for small objects, we propose to amplify the feature map so that detailed information hidden in the last feature map can be enriched. Finally, we design a joint training loss function including center loss for both horizontal and oriented bounding boxes, and reduce the impact of small inter-class diversity on vehicle detection. The proposed framework is evaluated on the VEDAI dataset that consists of 9 vehicle categories. The experimental results show that the proposed framework outperforms previous approaches with a mean average precision of 60.4% and 60.1% in detecting horizontal and oriented bounding boxes respectively, which is about 8% better than Faster RCNN.
Highlights
The development of high-resolution remote sensing images makes vehicle detection possible, which is important for autonomous driving and traffic monitoring [1,2,3]
Considering the shortcomings of feature representations for vehicles and limited discrimination ability of features for different categories in Faster RCNN, we propose an oriented vehicle detection method based on feature map amplification
To enhance the vehicle detection results, we propose an oriented vehicle detection method for aerial images consisting of three indispensable parts, namely oversampling and stitching data augmentation method, amplifying the feature map and a joint training loss function for horizontal and oriented bounding boxes with the center loss
Summary
The development of high-resolution remote sensing images makes vehicle detection possible, which is important for autonomous driving and traffic monitoring [1,2,3]. The oriented vehicle detection of multiple types is significant. The transportations such as car, tractor, vans, plane, pick-up and so on are the common vehicles existing in aerial images and they are studied in this paper. The other definition is that an object will be regarded as small objects if their sizes are below 32 × 32 pixels. In high-resolution remote sensing images, vehicles usually occupy a small area below 10% of the image size or smaller than 32 × 32 pixels.
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