Abstract
Oriented object detection in optical remote sensing images (ORSIs) is a challenging task since the targets in ORSIs are displayed in an arbitrarily oriented manner and on small scales, and are densely packed. Current state-of-the-art oriented object detection models used in ORSIs primarily evolved from anchor-based and direct regression-based detection paradigms. Nevertheless, they still encounter a design difficulty from handcrafted anchor definitions and learning complexities in direct localization regression. To tackle these issues, in this paper, we proposed a novel multi-sector oriented object detection framework called MSO2-Det, which quantizes the scales and orientation prediction of targets in ORSIs via an anchor-free classification-to-regression approach. Specifically, we first represented the arbitrarily oriented bounding box as four scale offsets and angles in four quadrant sectors of the corresponding Cartesian coordinate system. Then, we divided the scales and angle space into multiple discrete sectors and obtained more accurate localization information by a coarse-granularity classification to fine-grained regression strategy. In addition, to decrease the angular-sector classification loss and accelerate the network’s convergence, we designed a smooth angular-sector label (SASL) that smoothly distributes label values with a definite tolerance radius. Finally, we proposed a localization-aided detection score (LADS) to better represent the confidence of a detected box by combining the category-classification score and the sector-selection score. The proposed MSO2-Det achieves state-of-the-art results on three widely used benchmarks, including the DOTA, HRSC2016, and UCAS-AOD data sets.
Highlights
With the development of aerospace technology and sensor technology, remote sensing technology is entering a new stage that can quickly and accurately provide a variety of massive Earth observation data and facilitate widely applied research
The proposed representation of arbitrarily oriented bounding boxes (AOBBs) addresses the ambiguity problem of the boundary and the angle well, while enhancing the convergence performance of the network; We proposed a classification-to-regression strategy to obtain the accurate localization of the optical remote sensing images (ORSIs) targets with discrete scale and angular sectors
For an optical remote sensing image, anchor-based detectors first make use of many fixed anchors as a referee and either regress the localization offset of the bounding box or generate the region proposals on the basis of anchors and decide whether the corresponding proposal belongs to a certain category
Summary
With the development of aerospace technology and sensor technology, remote sensing technology is entering a new stage that can quickly and accurately provide a variety of massive Earth observation data and facilitate widely applied research. The proposed representation of AOBBs addresses the ambiguity problem of the boundary and the angle well, while enhancing the convergence performance of the network; We proposed a classification-to-regression strategy to obtain the accurate localization of the ORSI targets with discrete scale and angular sectors This strategy makes it easier for the network to learn the scale and orientation information of the AOBB; We designed a smooth angular-sector label (SASL) that smoothly distributes label values with a definite tolerance radius. With this label, the missed rate and detection accuracy are dramatically improved; To obtain a more accurate confidence of the detected boxes, we proposed the fusion of classification and localization information and achieved promising results on the DOTA, HRSC2016, and UCAS-AOD data sets.
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