Remote sensing image object detection based on rotatable bounding box

Zhuang, Wenhua; Tang, Xiaogang; Yang, Guangyu Robert; Yuan, Guangming; Yu, Haoyuan

doi:10.1109/dsins54396.2021.9670625

Cited by 2 publications

(5 citation statements)

References 12 publications

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“…where AP is the average accuracy of each category, obtained by integrating PRðrÞcurves which is combined with precision and recall, AP i represents the average precision in class i, and K represents the number of classes. We compare the ten peer techniques, including DCL [36], RSDet [23], GWD [37], KLD [38], R2-CNN [39], Rotate-YOLOv5 [29], RetinaNet [14], MR [40], CMR [41], and FR OBB [21] on DOTA-v1.5. Specifically, Zhuang et al [29] proposed Rotate-YOLOv5 which is one of our most relevant work.…”

Section: Implementation Detailsmentioning

confidence: 99%

“…We compare the ten peer techniques, including DCL [36], RSDet [23], GWD [37], KLD [38], R2-CNN [39], Rotate-YOLOv5 [29], RetinaNet [14], MR [40], CMR [41], and FR OBB [21] on DOTA-v1.5. Specifically, Zhuang et al [29] proposed Rotate-YOLOv5 which is one of our most relevant work. We proposed one-anchor-based method as new sampling strategy that can better balance the positive and negative samples of small targets.…”

Section: Implementation Detailsmentioning

confidence: 99%

“…Due to the complex background and the huge variation in the orientation, scale, and appearance of the object instances in remote sensing images, it is difficult to apply the horizontal detection algorithms to rotated object detec-tion. In order to predict the location and orientation of the rotated objects in remote sensing images, previous rotation detection algorithms [22][23][24][25][26][27][28][29] use preset rotation anchors and additional angle prediction. Owing to changes in orientation, numerous anchors should be preset on the feature map making them spatially aligned with GT boxes.…”

Section: Introductionmentioning

confidence: 99%

“…For example, RoI transformer [23] uses horizontal anchor points but learning the RoI of rotation through spatial transformations reduces the number of predefined anchors to some extent. Rotate-YOLOv5 [29] uses CIoU as the loss function of the bounding box and mosaic data enhancements to improve the detection accuracy on the basis of ensuring the detection speed. R3Det [30] recodes modules using cascading regression and redefinition boxes to achieve high performance.…”

Section: Introductionmentioning

confidence: 99%

“…The problem discussed is universal in detection algorithms. Inspired by FCOS [13], YOLO [4], ATSS [17], and Rotate-YOLOv5 [29], we analyze not only the characteristics of the existing mainstream algorithms for positive and negative sample sampling strategies but also the advantages of anchor-based and anchor-free methods. Meanwhile, we propose a remote sensing object detection algorithm based on a one anchor-based method.…”

Section: Introductionmentioning

confidence: 99%

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OAB-YOLOv5: One-Anchor-Based YOLOv5 for Rotated Object Detection in Remote Sensing Images

2022

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Remote sensing images are widely distributed, small in object size, and complex in background, resulting in low accuracy and slow speed of remote sensing image detection. Existing remote sensing object detection is generally based on the detector with anchors. With the proposal of a feature pyramid network (FPN) and focal loss, an anchorless detector emerges, however, the accuracy of anchorless detection is often low. First, this study analyzes the differences and characteristics of the intersection of union (IoU) and shape matchings based on anchors in mainstream algorithms and indicates that in dense or complex scenes, some labels are not easily assigned to positive samples, which leads to detection failure. Subsequently, we proposean one-anchor-based (OAB) object detection algorithm based on the idea of central point sampling in the anchor-free detector. The positive samples and negative samples are defined according to the central point sampling and distance constraint, and an anchor box is preset for each positive sample to accelerate its convergence. It reduces the complexity of the anchor-based detector, improves the inference speed, and reduces the setting of hyperparameters in the traditional matching strategy, rendering the model more flexible. Finally, in order to suppress background noise in remote sensing images, the vision transformer (ViT) is adopted to connect the neck and head, making it easier for the network to pay attention to key information. Thus, it is not easy to lose in the training process. Experiments on challenging public dataset—DOTA dataset- verified the effectiveness of the proposed algorithm. The experimental results show that the mAP of the optimized OAB-YOLOv5 method is improved by 2.79%, the number of parameters is reduced by 13.2%, and the inference time is reduced by 11% compared with the YOLOv5 baseline.

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Section: Implementation Detailsmentioning

confidence: 99%

Section: Implementation Detailsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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OAB-YOLOv5: One-Anchor-Based YOLOv5 for Rotated Object Detection in Remote Sensing Images

2022

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DAG-YOLO: A Context-Feature Adaptive fusion Rotating Detection Network in Remote Sensing Images

Guo,

He,

Yang

et al. 2024

ACM Trans. Multimedia Comput. Commun. Appl.

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Object detection in remote sensing image (RSI) research has seen significant advancements, particularly with the advent of deep learning. However, challenges such as orientation, scale, aspect ratio variations, dense object distribution, and category imbalances remain. To address these challenges, we present DAG-YOLO , a one-stage context-feature adaptive weighted fusion network that incorporates through three innovative parts. Firstly, we integrate 1D Gaussian Angle-coding with YOLOv5 to convert the angle regression task into a classification task, establishing a more robust rotating object detection baseline, GLR-YOLO . Secondly, we introduce the Dual Branch Context Adaptive Modeling module (DBCAM) , which enhances feature extraction capabilities by capturing global context information. Thirdly, we design an adaptive detect head with the Adaptive Global Feature Aggregation and Reweighting module (AGFAR) . AGFAR addresses feature inconsistency among different output layers of the Feature Pyramid Network (FPN), retaining useful semantic information and elevating detection accuracy. Extensive experiments on public datasets DOTA-v1.0, DOTA-v1.5, and UCAS-AOD showcase mAP scores of 77.75%, 73.79%, and 90.27% respectively. Our proposed method has the best performance among the current mainstream SOTA methods, which proves its effectiveness in RSI object detection.

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Remote sensing image object detection based on rotatable bounding box

Cited by 2 publications

References 12 publications

OAB-YOLOv5: One-Anchor-Based YOLOv5 for Rotated Object Detection in Remote Sensing Images

OAB-YOLOv5: One-Anchor-Based YOLOv5 for Rotated Object Detection in Remote Sensing Images

DAG-YOLO: A Context-Feature Adaptive fusion Rotating Detection Network in Remote Sensing Images

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