AIoU: Adaptive bounding box regression for accurate oriented object detection

2021 ◽  
Author(s):  
Nu Wen ◽  
Renzhong Guo ◽  
Ding Ma ◽  
Xiang Ye ◽  
Biao He
Keyword(s):  
Object Detection ◽  
Bounding Box ◽  
Oriented Object

scholarly journals Improved Oriented Object Detection in Remote Sensing Images Based on a Three-Point Regression Method

2021 ◽  
Vol 13 (22) ◽  
pp. 4517
Author(s):  
Falin Wu ◽  
Jiaqi He ◽  
Guopeng Zhou ◽  
Haolun Li ◽  
Yushuang Liu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Object Detection ◽  
Poor Performance ◽  
Regression Method ◽  
Remote Sensing Images ◽  
Sensing Applications ◽  
Bounding Box ◽  
Bounding Boxes ◽  
Fully Connected ◽  
Oriented Object

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.

scholarly journals Gliding vertex on the horizontal bounding box for multi-oriented object detection

2020 ◽  
pp. 1-1 ◽  
Author(s):  
Yongchao Xu ◽  
Mingtao Fu ◽  
Qimeng Wang ◽  
Yukang Wang ◽  
Kai Chen ◽  
...  
Keyword(s):  
Object Detection ◽  
Bounding Box ◽  
Oriented Object

scholarly journals Bounding Box Projection for Regression Uncertainty in Oriented Object Detection

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 58768-58779
Author(s):  
Qian Wu ◽  
Wangtao Xiang ◽  
Rui Tang ◽  
Jun Zhu
Keyword(s):  
Object Detection ◽  
Bounding Box ◽  
Oriented Object

Automatic accounting of Baikal diatomic algae: approaches and prospects

2019 ◽  
pp. 295-299
Author(s):  
Кonstantin А. Elshin ◽  
Еlena I. Molchanova ◽  
Мarina V. Usoltseva ◽  
Yelena V. Likhoshway
Keyword(s):  
Object Detection ◽  
Loss Function ◽  
Classification Accuracy ◽  
Diatom Species ◽  
Bounding Box ◽  
Synedra Acus ◽  
And Training

Using the TensorFlow Object Detection API, an approach to identifying and registering Baikal diatom species Synedra acus subsp. radians has been tested. As a result, a set of images was formed and training was conducted. It is shown that аfter 15000 training iterations, the total value of the loss function was obtained equal to 0,04. At the same time, the classification accuracy is equal to 95%, and the accuracy of construction of the bounding box is also equal to 95%.

scholarly journals CGP Box: An effective direction representation strategy for oriented object detection in remote sensing images

2021 ◽  
Vol 42 (17) ◽  
pp. 6670-6691
Author(s):  
Qiuyu Guan ◽  
Zhenshen Qu ◽  
Ming Zeng ◽  
Jianxiong Shen ◽  
Jingda Du
Keyword(s):  
Remote Sensing ◽  
Object Detection ◽  
Remote Sensing Images ◽  
Oriented Object

scholarly journals Automatic Roadway Features Detection with Oriented Object Detection

2021 ◽  
Vol 11 (8) ◽  
pp. 3531
Author(s):  
Hesham M. Eraqi ◽  
Karim Soliman ◽  
Dalia Said ◽  
Omar R. Elezaby ◽  
Mohamed N. Moustafa ◽  
...  
Keyword(s):  
Object Detection ◽  
Safety Evaluation ◽  
Autonomous Driving ◽  
Detection Accuracy ◽  
The Road ◽  
Detection Model ◽  
Detection Approach ◽  
Roadway Safety ◽  
Safety Features ◽  
Oriented Object

Extensive research efforts have been devoted to identify and improve roadway features that impact safety. Maintaining roadway safety features relies on costly manual operations of regular road surveying and data analysis. This paper introduces an automatic roadway safety features detection approach, which harnesses the potential of artificial intelligence (AI) computer vision to make the process more efficient and less costly. Given a front-facing camera and a global positioning system (GPS) sensor, the proposed system automatically evaluates ten roadway safety features. The system is composed of an oriented (or rotated) object detection model, which solves an orientation encoding discontinuity problem to improve detection accuracy, and a rule-based roadway safety evaluation module. To train and validate the proposed model, a fully-annotated dataset for roadway safety features extraction was collected covering 473 km of roads. The proposed method baseline results are found encouraging when compared to the state-of-the-art models. Different oriented object detection strategies are presented and discussed, and the developed model resulted in improving the mean average precision (mAP) by 16.9% when compared with the literature. The roadway safety feature average prediction accuracy is 84.39% and ranges between 91.11% and 63.12%. The introduced model can pervasively enable/disable autonomous driving (AD) based on safety features of the road; and empower connected vehicles (CV) to send and receive estimated safety features, alerting drivers about black spots or relatively less-safe segments or roads.

Rotational multipyramid network with bounding‐box transformation for object detection

2021 ◽  
Author(s):  
Donggeun Kim ◽  
San Kim ◽  
Siheon Jeong ◽  
Ji‐Wan Ham ◽  
Seho Son ◽  
...  
Keyword(s):  
Object Detection ◽  
Bounding Box

Multi-branch Bounding Box Regression for Object Detection

2022 ◽  
Author(s):  
Hui-Shen Yuan ◽  
Si-Bao Chen ◽  
Bin Luo ◽  
Hao Huang ◽  
Qiang Li
Keyword(s):  
Object Detection ◽  
Bounding Box
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