Dense Label Encoding for Boundary Discontinuity Free Rotation Detection

Yang, Xue; Hou, Liping; Zhou, Yue; Wang, Wentao; Yan, Junchi

doi:10.1109/cvpr46437.2021.01556

Cited by 234 publications

(111 citation statements)

References 39 publications

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“…Being observed from an overhead perspective, the objects in aerial images present more diversified orientations. Oriented object detection [1]- [5], [36]- [44] is an extension of horizontal object detection to accurately outline the objects, especially those with large aspect ratios.…”

Section: Related Workmentioning

confidence: 99%

“…For fast and accurate oriented object detection, R 3 Det [42] and O 2 -DNet [43] make attempts in one-stage model with RetinaNet and anchor free structures. Based on R 3 Det, R 3 Det-DCL [5] designs Densely Coded Labels (DCL) for angle classification, which replaces the Sparsely Coded Label (SCL) in classification-based detectors before, and reduces three times training speed, further bringing notable improvements in accuracy of detection tasks. What's more, for oriented object detection, SCRDet [4] combines pixel and channel attention network for small and cluttered objects.…”

Section: Related Workmentioning

confidence: 99%

“…O BJECT detection in aerial images is one of the most fundamental yet challenging research tasks, which aims to assign a bounding box with a unique semantic category label to each surficial object in the given aerial images [1]- [5]. This task is critical for a wide range of downstream tasks, e.g., land resource management, ecological monitoring, and land ecosystem evaluation [6], [7].…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, it is difficult to directly achieve a satisfying recognition performance in aerial images using the existing natural scene object detectors. To this end, state-of-the-art methods focus on developing effective head networks [1], adaptive dense anchor generators [2], and labeling strategy [3], [5]. Besides, effective feature learning strategies play a crucial role.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Learning Calibrated-Guidance for Object Detection in Aerial Images

Liang¹,

Wei²,

Zhang³

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Object detection is one of the most fundamental yet challenging research topics in the domain of computer vision. Recently, the study on this topic in aerial images has made tremendous progress. However, complex background and worse imaging quality are obvious problems in aerial object detection. Most state-of-the-art approaches tend to develop elaborate attention mechanisms for the space-time feature calibrations with arduous computational complexity, while surprisingly ignoring the importance of feature calibrations in channel-wise. In this work, we propose a simple yet effective Calibrated-Guidance (CG) scheme to enhance channel communications in a feature transformer fashion, which can adaptively determine the calibration weights for each channel based on the global feature affinity correlations. Specifically, for a given set of feature maps, CG first computes the feature similarity between each channel and the remaining channels as the intermediary calibration guidance. Then, re-representing each channel by aggregating all the channels weighted together via the guidance operation. Our CG is a general module that can be plugged into any deep neural networks, which is named as CG-Net. To demonstrate its effectiveness and efficiency, extensive experiments are carried out on both oriented object detection task and horizontal object detection task in aerial images. Experimental results on two challenging benchmarks (i.e., DOTA and HRSC2016) demonstrate that our CG-Net can achieve the new state-of-the-art performance in accuracy with a fair computational overhead. The source code has been open sourced at https://github.com/WeiZongqi/CG-Net

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Learning Calibrated-Guidance for Object Detection in Aerial Images

Liang¹,

Wei²,

Zhang³

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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show abstract

“…Comparison of peer methods. Table 5 compares the six peer techniques, including IoU-Smooth L1 Loss [3], Modulated loss [43], RIL [32], CSL [4], DCL [44], and GWD [5] on DOTA-v1.0. For fairness, these methods are all implemented on the same baseline method, and are trained and tested under the same environment and hyperparameters.…”

Section: Ablation Study and Further Comparisonmentioning

confidence: 99%

Learning High-Precision Bounding Box for Rotated Object Detection via Kullback-Leibler Divergence

Yang¹,

Yang²,

Yang³

et al. 2021

Preprint

Self Cite

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Existing rotated object detectors are mostly inherited from the horizontal detection paradigm, as the latter has evolved into a well-developed area. However, these detectors are difficult to perform prominently in high-precision detection due to the limitation of current regression loss design, especially for objects with large aspect ratios. Taking the perspective that horizontal detection is a special case for rotated object detection, in this paper, we are motivated to change the design of rotation regression loss from induction paradigm to deduction methodology, in terms of the relation between rotation and horizontal detection. We show that one essential challenge is how to modulate the coupled parameters in the rotation regression loss, as such the estimated parameters can influence to each other during the dynamic joint optimization, in an adaptive and synergetic way. Specifically, we first convert the rotated bounding box into a 2-D Gaussian distribution, and then calculate the Kullback-Leibler Divergence (KLD) between the Gaussian distributions as the regression loss. By analyzing the gradient of each parameter, we show that KLD (and its derivatives) can dynamically adjust the parameter gradients according to the characteristics of the object. It will adjust the importance (gradient weight) of the angle parameter according to the aspect ratio. This mechanism can be vital for high-precision detection as a slight angle error would cause a serious accuracy drop for large aspect ratios objects. More importantly, we have proved that KLD is scale invariant. We further show that the KLD loss can be degenerated into the popular l n -norm loss for horizontal detection. Experimental results on seven datasets using different detectors show its consistent superiority, and codes are available at https://github.com/yangxue0827/RotationDetection.

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Feature Adaption with Predicted Boxes for Oriented Object Detection in Aerial Images

Zou

Guan

et al. 2021

Lecture Notes in Computer Science

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Dense Label Encoding for Boundary Discontinuity Free Rotation Detection

Cited by 234 publications

References 39 publications

Learning Calibrated-Guidance for Object Detection in Aerial Images

Learning Calibrated-Guidance for Object Detection in Aerial Images

Learning High-Precision Bounding Box for Rotated Object Detection via Kullback-Leibler Divergence

Feature Adaption with Predicted Boxes for Oriented Object Detection in Aerial Images

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