1,250 research outputs found
Oriented Object Detection in Optical Remote Sensing Images using Deep Learning: A Survey
Oriented object detection is one of the most fundamental and challenging
tasks in remote sensing, aiming at locating the oriented objects of numerous
predefined object categories. Recently, deep learning based methods have
achieved remarkable performance in detecting oriented objects in optical remote
sensing imagery. However, a thorough review of the literature in remote sensing
has not yet emerged. Therefore, we give a comprehensive survey of recent
advances and cover many aspects of oriented object detection, including problem
definition, commonly used datasets, evaluation protocols, detection frameworks,
oriented object representations, and feature representations. Besides, the
state-of-the-art methods are analyzed and discussed. We finally discuss future
research directions to put forward some useful research guidance. We believe
that this survey shall be valuable to researchers across academia and industr
Dynamic Anchor Learning for Arbitrary-Oriented Object Detection
Arbitrary-oriented objects widely appear in natural scenes, aerial
photographs, remote sensing images, etc., thus arbitrary-oriented object
detection has received considerable attention. Many current rotation detectors
use plenty of anchors with different orientations to achieve spatial alignment
with ground truth boxes, then Intersection-over-Union (IoU) is applied to
sample the positive and negative candidates for training. However, we observe
that the selected positive anchors cannot always ensure accurate detections
after regression, while some negative samples can achieve accurate
localization. It indicates that the quality assessment of anchors through IoU
is not appropriate, and this further lead to inconsistency between
classification confidence and localization accuracy. In this paper, we propose
a dynamic anchor learning (DAL) method, which utilizes the newly defined
matching degree to comprehensively evaluate the localization potential of the
anchors and carry out a more efficient label assignment process. In this way,
the detector can dynamically select high-quality anchors to achieve accurate
object detection, and the divergence between classification and regression will
be alleviated. With the newly introduced DAL, we achieve superior detection
performance for arbitrary-oriented objects with only a few horizontal preset
anchors. Experimental results on three remote sensing datasets HRSC2016, DOTA,
UCAS-AOD as well as a scene text dataset ICDAR 2015 show that our method
achieves substantial improvement compared with the baseline model. Besides, our
approach is also universal for object detection using horizontal bound box. The
code and models are available at https://github.com/ming71/DAL.Comment: Accepted to AAAI 2021. The code and models are available at
https://github.com/ming71/DA
Anchor Retouching via Model Interaction for Robust Object Detection in Aerial Images
Object detection has made tremendous strides in computer vision. Small object
detection with appearance degradation is a prominent challenge, especially for
aerial observations. To collect sufficient positive/negative samples for
heuristic training, most object detectors preset region anchors in order to
calculate Intersection-over-Union (IoU) against the ground-truthed data. In
this case, small objects are frequently abandoned or mislabeled. In this paper,
we present an effective Dynamic Enhancement Anchor (DEA) network to construct a
novel training sample generator. Different from the other state-of-the-art
techniques, the proposed network leverages a sample discriminator to realize
interactive sample screening between an anchor-based unit and an anchor-free
unit to generate eligible samples. Besides, multi-task joint training with a
conservative anchor-based inference scheme enhances the performance of the
proposed model while reducing computational complexity. The proposed scheme
supports both oriented and horizontal object detection tasks. Extensive
experiments on two challenging aerial benchmarks (i.e., DOTA and HRSC2016)
indicate that our method achieves state-of-the-art performance in accuracy with
moderate inference speed and computational overhead for training. On DOTA, our
DEA-Net which integrated with the baseline of RoI-Transformer surpasses the
advanced method by 0.40% mean-Average-Precision (mAP) for oriented object
detection with a weaker backbone network (ResNet-101 vs ResNet-152) and 3.08%
mean-Average-Precision (mAP) for horizontal object detection with the same
backbone. Besides, our DEA-Net which integrated with the baseline of ReDet
achieves the state-of-the-art performance by 80.37%. On HRSC2016, it surpasses
the previous best model by 1.1% using only 3 horizontal anchors
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