Abstract
Object detection in aerial images is a fundamental yet challenging task in remote sensing field. As most objects in aerial images are in arbitrary orientations, oriented bounding boxes (OBBs) have a great superiority compared with traditional horizontal bounding boxes (HBBs). However, the regression-based OBB detection methods always suffer from ambiguity in the definition of learning targets, which will decrease the detection accuracy. In this paper, we provide a comprehensive analysis of OBB representations and cast the OBB regression as a pixel-level classification problem, which can largely eliminate the ambiguity. The predicted masks are subsequently used to generate OBBs. To handle huge scale changes of objects in aerial images, an Inception Lateral Connection Network (ILCN) is utilized to enhance the Feature Pyramid Network (FPN). Furthermore, a Semantic Attention Network (SAN) is adopted to provide the semantic feature, which can help distinguish the object of interest from the cluttered background effectively. Empirical studies show that the entire method is simple yet efficient. Experimental results on two widely used datasets, i.e., DOTA and HRSC2016, demonstrate that the proposed method outperforms state-of-the-art methods.
Highlights
We compare our method with the state-of-the-art methods on oriented bounding boxes (OBBs) and horizontal bounding boxes (HBBs) tasks of DOTA dataset in Tables 3 and 4
By using ResNet-50, our method achieves 74.86% and 75.98% mean Average Precision (mAP) on OBB task of DOTA, respectively, and outperforms all methods which even use ResNet-101
We analyzed the influence of different OBB representations for oriented object detection in aerial images, which exposes shortcomings of the typical regression-based OBB representation methods like θ-based, point-based and h-based OBB representation methods
Summary
With the development of deep learning technology, modern generic object detection methods based on a horizontal bounding box (HBB) have achieved great success in natural scenes. They can be organized into two main categories: two-stage and single-stage detectors. Faster R-CNN [9] introduces a Region Proposal Network (RPN) to generate the region proposals efficiently. Some researchers further extend the work of Faster R-CNN for better performance, like Region-based Fully Convolutional Network (R-FCN) [10], Deformable R-FCN [11], Light Head R-CNN [12], Scale Normalization for Image Pyramids (SNIP) [13], SNIP with Efficient Resampling (SNIPER) [14], etc. Compared with two-stage detectors, one-stage detectors are much simpler and more efficient, because there is no need to produce region proposals
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