Abstract
Oriented object detection in remote sensing images (RSIs) is a significant yet challenging Earth Vision task, as the objects in RSIs usually emerge with complicated backgrounds, arbitrary orientations, multi-scale distributions, and dramatic aspect ratio variations. Existing oriented object detectors are mostly inherited from the anchor-based paradigm. However, the prominent performance of high-precision and real-time detection with anchor-based detectors is overshadowed by the design limitations of tediously rotated anchors. By using the simplicity and efficiency of keypoint-based detection, in this work, we extend a keypoint-based detector to the task of oriented object detection in RSIs. Specifically, we first simplify the oriented bounding box (OBB) as a center-based rotated inscribed ellipse (RIE), and then employ six parameters to represent the RIE inside each OBB: the center point position of the RIE, the offsets of the long half axis, the length of the short half axis, and an orientation label. In addition, to resolve the influence of complex backgrounds and large-scale variations, a high-resolution gated aggregation network (HRGANet) is designed to identify the targets of interest from complex backgrounds and fuse multi-scale features by using a gated aggregation model (GAM). Furthermore, by analyzing the influence of eccentricity on orientation error, eccentricity-wise orientation loss (ewoLoss) is proposed to assign the penalties on the orientation loss based on the eccentricity of the RIE, which effectively improves the accuracy of the detection of oriented objects with a large aspect ratio. Extensive experimental results on the DOTA and HRSC2016 datasets demonstrate the effectiveness of the proposed method.
Highlights
With the fast-paced development of unmanned aerial vehicles (UAVs) and remote sensing technology, the analysis of remote sensing images (RSIs) has been increasingly applied in fields such as land surveying, environmental monitoring, intelligent transportation, seabed mapping, heritage site reconstruction, and so on [1,2,3,4,5,6]
As an angle-free oriented bounding box (OBB) definition, the rotated inscribed ellipse (RIE) effectively eliminates the angle periodicity and address the boundary case issues; We design a high-resolution gated aggregation network to capture the objects of interest from complicated backgrounds and integrate different scale features by implementing multi-scale parallel interactions and gated aggregation fusion; We propose an eccentricity-wise orientation loss function to fix the sensitivity of the eccentricity of the ellipse to the orientation error and effectively improve the accuracy of the detection of slender oriented objects with large aspect ratios; We perform extensive experiments to verify the advanced performance compared with state-of-the-art oriented object detectors on remote sensing datasets
RRD [41] introduces an activate rotating filter (ARF) and boosts the performance to 82.89% average precision (AP). In addition to these three ship detectors, we compared our method with six other state-of-the-art anchor-based ship detectors, which were introduced in Section 2, i.e., R2 CNN [27], RRPN [28], R2 PN [31], RoI Trans [30], R3 Det [33], and S2 A-Net [35]
Summary
With the fast-paced development of unmanned aerial vehicles (UAVs) and remote sensing technology, the analysis of remote sensing images (RSIs) has been increasingly applied in fields such as land surveying, environmental monitoring, intelligent transportation, seabed mapping, heritage site reconstruction, and so on [1,2,3,4,5,6]. Object detection in RSIs is regarded as a high-level computer vision task with the purpose of pinpointing the targets in RSIs. Due to the characteristics of remote sensing targets, such as complex backgrounds, huge aspect ratios, multiple scales, and variations of orientations, remote sensing object detection remains a challenging and significant research issue. Remote sensing images are typically taken with bird’s-eye views, and horizontal-detection-based methods will experience significant performance degradation when applied directly to remote sensing images, largely owing to the distinctive appearances and characteristics
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