Oriented R-CNN for Object Detection

Xie, Xingxing; Cheng, Gong; Wang, Jiabao; Yao, Xiwen; Han, Junwei

doi:10.1109/iccv48922.2021.00350

Cited by 635 publications

(268 citation statements)

References 35 publications

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“…We use one of the SOTA OBB detection frameworks -ORCN [109] to evaluate the performance of different pretrained backbones. We adopt the default hyper-parameters of ORCN, which is implemented in OBBDetection 2 .…”

Section: Aerial Object Detectionmentioning

confidence: 99%

An Empirical Study of Remote Sensing Pretraining

Wang¹,

Zhang²,

Du³

et al. 2022

Preprint

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Section: Aerial Object Detectionmentioning

confidence: 99%

An Empirical Study of Remote Sensing Pretraining

Wang¹,

Zhang²,

Du³

et al. 2022

Preprint

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“…In the experiment part, there are six comparison networks, namely faster R-CNN [8], oriented R-CNN [16], ReDet [25], RoITransformer [17], double-head [15], and IPC-Det (ours). There are four experimental indicators: "mAP", "AP", "AR", "Params", "FPS".…”

Section: Comparision With Other Networkmentioning

confidence: 99%

“…Currently, deep learning is still the mainstream method for object detection [4][5][6][7]. In the field of remote sensing, object detection based on deep learning can be divided into two categories according to the type of bounding box: HBB detection [8][9][10][11][12][13][14] and OBB detection [15][16][17][18][19][20]. HBB detection is the most commonly used object detection method.…”

Section: Introductionmentioning

confidence: 99%

“…OBB detectors are widely used in aerial images and ship detection [16,17,[19][20][21][22][23][24]. Rotated RoI Align (RRoI Align) is used to extract rotation-invariant features, which can warp region features precisely according to the bounding boxes of Rotated RoI (RRoI) in the 2D planar [17].…”

Section: Introductionmentioning

confidence: 99%

“…Figure 10. Visualization results of six types of networks on the dataset: (a) is the partial result of faster R-CNN [8], (b) is the partial result of oriented R-CNN[16], (c) is the partial result of RoITransformer[17], (d) is the partial result of double-head[15], (e) is the partial result of ReDet[25], and (f) is the partial result of IPC-Det (ours).…”

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confidence: 99%

See 2 more Smart Citations

Imaging Parameters-Considered Slender Target Detection in Optical Satellite Images

et al. 2022

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The existing slender target detection methods based on optical satellite images are greatly affected by the satellite perspective and the solar perspective. Due to limited data sources, it is difficult to implement a fully data-driven approach. This work introduces the imaging parameters of optical satellite images, which greatly reduces the influence of the satellite perspectives and the solar perspectives, and reduces the demand for the amount of data. We improve the oriented bounding box (OBB) detector based on faster R-CNN (region convolutional neural networks) and propose an imaging parameters-considered detector (IPC-Det) which is more suitable for our task. Specifically, in the first stage, the umbra and the shadow are extracted by horizontal bounding box (HBB), respectively, and then the matching of the umbra and the shadow is realized according to the imaging parameters. In the second stage, the paired umbra and shadow features are used to complete the classification and regression, and the target is obtained by OBB. In experiments, after introducing imaging parameters, our detection accuracy is improved by 3.9% (up to 87.5%), proving that this work is a successful attempt to introduce imaging parameters for slender target detection.

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Toward object alphabet augmentation for object detection in very high‐resolution satellite images

Laban

Abdellatif

Ebeid

et al. 2021

Concurrency and Computation

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Object detection in very high-resolution satellite images has become an important tool in many fields. So there is a vital need to build a more precise and accurate detector. There is a great challenge to detect very small and condensed objects with their different semantics shapes. Recently, convolutional neural networks with different structures have achieved awesome performance. We propose object alphabet augmentation method that extracts object instants during the training process to form an alphabet for these objects in a newly generated dataset. The proposed method uses the DarkNet-53 framework to get the main features for each object using two new generated datasets for alphabet augmentation up-down where objects are placed in up down, and alphabet augmentation horizontal where objects are placed in their original setting. Experiments are conducted on datasets randomly generated from the DOTA dataset. The experimental results show that the proposed method has improved the accuracy of detection of the target objects of the DOTA dataset for most object classes, especially for the up-down dataset.

show abstract

Oriented R-CNN for Object Detection

Cited by 635 publications

References 35 publications

An Empirical Study of Remote Sensing Pretraining

An Empirical Study of Remote Sensing Pretraining

Imaging Parameters-Considered Slender Target Detection in Optical Satellite Images

Toward object alphabet augmentation for object detection in very high‐resolution satellite images

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