10,358 research outputs found
Metric-aligned Sample Selection and Critical Feature Sampling for Oriented Object Detection
Arbitrary-oriented object detection is a relatively emerging but challenging
task. Although remarkable progress has been made, there still remain many
unsolved issues due to the large diversity of patterns in orientation, scale,
aspect ratio, and visual appearance of objects in aerial images. Most of the
existing methods adopt a coarse-grained fixed label assignment strategy and
suffer from the inconsistency between the classification score and localization
accuracy. First, to align the metric inconsistency between sample selection and
regression loss calculation caused by fixed IoU strategy, we introduce affine
transformation to evaluate the quality of samples and propose a distance-based
label assignment strategy. The proposed metric-aligned selection (MAS) strategy
can dynamically select samples according to the shape and rotation
characteristic of objects. Second, to further address the inconsistency between
classification and localization, we propose a critical feature sampling (CFS)
module, which performs localization refinement on the sampling location for
classification task to extract critical features accurately. Third, we present
a scale-controlled smooth loss (SC-Loss) to adaptively select high
quality samples by changing the form of regression loss function based on the
statistics of proposals during training. Extensive experiments are conducted on
four challenging rotated object detection datasets DOTA, FAIR1M-1.0, HRSC2016,
and UCAS-AOD. The results show the state-of-the-art accuracy of the proposed
detector
Evaluation of Environmental Conditions on Object Detection using Oriented Bounding Boxes for AR Applications
The objective of augmented reality (AR) is to add digital content to natural
images and videos to create an interactive experience between the user and the
environment. Scene analysis and object recognition play a crucial role in AR,
as they must be performed quickly and accurately. In this study, a new approach
is proposed that involves using oriented bounding boxes with a detection and
recognition deep network to improve performance and processing time. The
approach is evaluated using two datasets: a real image dataset (DOTA dataset)
commonly used for computer vision tasks, and a synthetic dataset that simulates
different environmental, lighting, and acquisition conditions. The focus of the
evaluation is on small objects, which are difficult to detect and recognise.
The results indicate that the proposed approach tends to produce better Average
Precision and greater accuracy for small objects in most of the tested
conditions.Comment: 11 pages, 4 figures, conferenc
Evaluation of Environmental Conditions on Object Detection Using Oriented Bounding Boxes for AR Applications
The objective of augmented reality (AR) is to add digital content to natural images and videos to create an interactive experience between the user and the environment. Scene analysis and object recognition play a crucial role in AR, as they must be performed quickly and accurately. In this study, a new approach is proposed that involves using oriented bounding boxes with a detection and recognition deep network to improve performance and processing time. The approach is evaluated using two datasets: a real image dataset (DOTA dataset) commonly used for computer vision tasks, and a synthetic dataset that simulates different environmental, lighting, and acquisition conditions. The focus of the evaluation is on small objects, which are difficult to detect and recognise. The results indicate that the proposed approach tends to produce better Average Precision and greater accuracy for small objects in most of the tested conditions
PP-YOLOE-R: An Efficient Anchor-Free Rotated Object Detector
Arbitrary-oriented object detection is a fundamental task in visual scenes
involving aerial images and scene text. In this report, we present PP-YOLOE-R,
an efficient anchor-free rotated object detector based on PP-YOLOE. We
introduce a bag of useful tricks in PP-YOLOE-R to improve detection precision
with marginal extra parameters and computational cost. As a result,
PP-YOLOE-R-l and PP-YOLOE-R-x achieve 78.14 and 78.28 mAP respectively on DOTA
1.0 dataset with single-scale training and testing, which outperform almost all
other rotated object detectors. With multi-scale training and testing,
PP-YOLOE-R-l and PP-YOLOE-R-x further improve the detection precision to 80.02
and 80.73 mAP. In this case, PP-YOLOE-R-x surpasses all anchor-free methods and
demonstrates competitive performance to state-of-the-art anchor-based two-stage
models. Further, PP-YOLOE-R is deployment friendly and PP-YOLOE-R-s/m/l/x can
reach 69.8/55.1/48.3/37.1 FPS respectively on RTX 2080 Ti with TensorRT and
FP16-precision. Source code and pre-trained models are available at
https://github.com/PaddlePaddle/PaddleDetection, which is powered by
https://github.com/PaddlePaddle/Paddle.Comment: 6 pages, 2 figures, 3 table
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
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