Spatial-Coordinate Attention and Multi-Path Residual Block Based Oriented Object Detection in Remote Sensing Images

Object detection in remote sensing images plays an important role in both military and civilian remote sensing applications. Objects in remote sensing images are different from those in natural images. They have the characteristics of scale diversity, arbitrary directivity, and dense arrangement, which causes difficulties in object detection. For objects with a large aspect ratio and that are oblique and densely arranged, using an oriented bounding box can help to avoid deleting some correct detection bounding boxes by mistake. The classic rotational region convolutional neural network (R2CNN) has advantages for text detection. However, R2CNN has poor performance in the detection of slender objects with arbitrary directivity in remote sensing images, and its fault tolerance rate is low. In order to solve this problem, this paper proposes an improved R2CNN based on a double detection head structure and a three-point regression method, namely, TPR-R2CNN. The proposed network modifies the original R2CNN network structure by applying a double fully connected (2-fc) detection head and classification fusion. One detection head is for classification and horizontal bounding box regression, the other is for classification and oriented bounding box regression. The three-point regression method (TPR) is proposed for oriented bounding box regression, which determines the positions of the oriented bounding box by regressing the coordinates of the center point and the first two vertices. The proposed network was validated on the DOTA-v1.5 and HRSC2016 datasets, and it achieved a mean average precision (mAP) of 3.90% and 15.27%, respectively, from feature pyramid network (FPN) baselines with a ResNet-50 backbone.

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Application of an improved oriented object detection algorithm in remote sensing images

10.1109/icwcsg53609.2021.00014 ◽

2021 ◽

Author(s):

Guozhi Miao ◽

Xiaokang Ren ◽

Ruchuan Guo ◽

Zhichao Peng

Keyword(s):

Remote Sensing ◽

Object Detection ◽

Detection Algorithm ◽

Remote Sensing Images ◽

Oriented Object

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Arbitrary-Oriented Object Detection in Remote Sensing Images Based on Polar Coordinates

IEEE Access ◽

10.1109/access.2020.3041025 ◽

2020 ◽

Vol 8 ◽

pp. 223373-223384

Author(s):

Lin Zhou ◽

Haoran Wei ◽

Hao Li ◽

Wenzhe Zhao ◽

Yi Zhang ◽

...

Keyword(s):

Remote Sensing ◽

Object Detection ◽

Polar Coordinates ◽

Remote Sensing Images ◽

Oriented Object

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A Lightweight Keypoint-Based Oriented Object Detection of Remote Sensing Images

Remote Sensing ◽

10.3390/rs13132459 ◽

2021 ◽

Vol 13 (13) ◽

pp. 2459

Author(s):

Yangyang Li ◽

Heting Mao ◽

Ruijiao Liu ◽

Xuan Pei ◽

Licheng Jiao ◽

...

Keyword(s):

Remote Sensing ◽

Object Detection ◽

Large Scale ◽

Detection Methods ◽

Gaussian Kernel ◽

Remote Sensing Images ◽

Computational Overhead ◽

Comparable Performance ◽

Bounding Boxes ◽

Oriented Object

Object detection in remote sensing images has been widely used in military and civilian fields and is a challenging task due to the complex background, large-scale variation, and dense arrangement in arbitrary orientations of objects. In addition, existing object detection methods rely on the increasingly deeper network, which increases a lot of computational overhead and parameters, and is unfavorable to deployment on the edge devices. In this paper, we proposed a lightweight keypoint-based oriented object detector for remote sensing images. First, we propose a semantic transfer block (STB) when merging shallow and deep features, which reduces noise and restores the semantic information. Then, the proposed adaptive Gaussian kernel (AGK) is adapted to objects of different scales, and further improves detection performance. Finally, we propose the distillation loss associated with object detection to obtain a lightweight student network. Experiments on the HRSC2016 and UCAS-AOD datasets show that the proposed method adapts to different scale objects, obtains accurate bounding boxes, and reduces the influence of complex backgrounds. The comparison with mainstream methods proves that our method has comparable performance under lightweight.

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Learning Higher-quality Rotation Invariance Features for Multi-oriented Object Detection in Remote Sensing Images

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing ◽

10.1109/jstars.2021.3085665 ◽

2021 ◽

pp. 1-1

Author(s):

Caiguang Zhang ◽

Boli Xiong ◽

Jinqian Zhang ◽

Xiao Li ◽

Gangyao Kuang

Keyword(s):

Remote Sensing ◽

Object Detection ◽

Rotation Invariance ◽

Remote Sensing Images ◽

Oriented Object

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Learning Rotated Inscribed Ellipse for Oriented Object Detection in Remote Sensing Images

Remote Sensing ◽

10.3390/rs13183622 ◽

2021 ◽

Vol 13 (18) ◽

pp. 3622

Author(s):

Xu He ◽

Shiping Ma ◽

Linyuan He ◽

Le Ru ◽

Chen Wang

Keyword(s):

Remote Sensing ◽

Aspect Ratio ◽

Object Detection ◽

Large Scale ◽

Large Aspect Ratio ◽

Remote Sensing Images ◽

Orientation Error ◽

Multi Scale ◽

Half Axis ◽

Oriented Object

Oriented object detection in remote sensing images (RSIs) is a significant yet challenging Earth Vision task, as the objects in RSIs usually emerge with complicated backgrounds, arbitrary orientations, multi-scale distributions, and dramatic aspect ratio variations. Existing oriented object detectors are mostly inherited from the anchor-based paradigm. However, the prominent performance of high-precision and real-time detection with anchor-based detectors is overshadowed by the design limitations of tediously rotated anchors. By using the simplicity and efficiency of keypoint-based detection, in this work, we extend a keypoint-based detector to the task of oriented object detection in RSIs. Specifically, we first simplify the oriented bounding box (OBB) as a center-based rotated inscribed ellipse (RIE), and then employ six parameters to represent the RIE inside each OBB: the center point position of the RIE, the offsets of the long half axis, the length of the short half axis, and an orientation label. In addition, to resolve the influence of complex backgrounds and large-scale variations, a high-resolution gated aggregation network (HRGANet) is designed to identify the targets of interest from complex backgrounds and fuse multi-scale features by using a gated aggregation model (GAM). Furthermore, by analyzing the influence of eccentricity on orientation error, eccentricity-wise orientation loss (ewoLoss) is proposed to assign the penalties on the orientation loss based on the eccentricity of the RIE, which effectively improves the accuracy of the detection of oriented objects with a large aspect ratio. Extensive experimental results on the DOTA and HRSC2016 datasets demonstrate the effectiveness of the proposed method.

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