Object detection, 데이콘 위성이미지 객체 검출 대회에서 2등을 수상한 TGO7 팀의 솔루션입니다.

1. TEAM TGO7
Joonhyung Park
Yunji Seo
Aerial Image Object Detection
Gliding Vertex, The special method to calculate the rounding bounding box effectively

2. Contents
1) Background
- Two method to calculate the bounding box in object detection
2) Data Processing Techniques
- Features and preprocessing methods of competition dataset
3) Modeling Techniques
- Detector model structure and performance improvement methods
4) Conclusion and Acknowledgments
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3. Background
Traditional object detection based on HBOX (Horizontal Bounding Box)●
1) Square pixel-based digital image 2) ROI calculation through RPN 3) Filtering through Threshold/NMS
Region Proposal NetworkRegion Of Interest Non-MaximumSuppression
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4. Background
Disadvantage of HBOX in aerial/remote images●
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5. Background
●
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Disadvantage of HBOX in aerial/remote images

6. Background
●
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Disadvantage of HBOX in aerial/remote images

7. Background
●
NMS
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Disadvantage of HBOX in aerial/remote images

8. Background
Improvement from HBOX to RBOX in aerial images●
HBOX (x, y, w, h)
RBOX (x, y, w, h, θ)
Center Point(x,y)
Height (h)
Angle (θ)
Width (w)
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9. Background
However, RBOX also has its drawbacks●
HBOX (x, y, w, h)
RBOX (x, y, w, h, θ)
Center Point(x,y)
Height (h)
Angle (θ)
Width (w)
𝟏
𝟏𝟎
𝝅
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10. Data Processing Techniques
Two features of this competition’s aerial image dataset●
1) Image sizes are very large (3000, 3000) 2) Unbalanced class dataset (4 classes)
67.3 %
22.4 %
0.2 %
10.1 %
Maritime Vessels
Container
Oil Tanker
Aircraft Carrier
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11. Data Processing Techniques
Three preprocessing method of aerial image dataset●
1) Large Image Split and Re-Merge
※ Prevention of object loss by overlap
2) Class balancing by oversampling
X 3
X 100
X 6
Maritime vessels Container
Oil Tanker Aircraft Carrier
X 1
3) Image Augmentation (Rotation, Scaling)
※ Color/Brightnessconversion had poor performance
…
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12. New ingenious method to calculate RBOX (Rotated Bounding Box)●
Modeling Techniques
Angle (θ)
Convert HBOX to RBOX using Angle RBOX using Vertex Distance Ratio
* Gliding Vertex
Stability Flexibility
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13. Measure object obliquity factor → Utilize HBOX & RBOX together●
Modeling Techniques
HBOX
RBOX
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14. Basic detector model, Faster-RCNN with Gliding Vertex●
Modeling Techniques
Various Scale Search by FPN
* Feature Pyramid Network Faster-RCNN with Gliding Vertex (9 variables output)
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15. Ensemble the model trained with various scale images●
Modeling Techniques (additional)
Object Scale by Class
Division Scale
COUNT 17850
MEAN 13741.3
STD 29926.8
MIN 110.5
25% 2295.0
50% 4369.8
75% 10815.0
MAX 436112.5
Statistics
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(Unit : Pixel x Pixel)

16. Ensemble the model trained with various scale images●
Modeling Techniques (additional)
Before Ensemble (1 Detector) After Ensemble (2 Detector)
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17. Ensemble the model trained with various scale images●
Modeling Techniques (additional)
Before Ensemble (1 Detector) After Ensemble (2 Detector)
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18. Ensemble the model trained with various scale images●
Modeling Techniques (additional)
Before Ensemble (1 Detector) After Ensemble (2 Detector)
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19. Performance improvement with Methods & Data Processing●
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Methods Data Processing mAP Score Remark
Faster RCNN
+ RBOX with Angle
Image Split & Merge 0.309 Baseline
Faster RCNN
+ Feature Pyramid Network
+ RBOX with Gliding Vertex
Image Split & Merge
+ Class Balancing
+ Augmentation
0.750
( 0.441)
Team Score
Faster RCNN
+ Feature Pyramid Network
+ RBOX with Gliding Vertex
+ Ensemble with various scales
Image Split & Merge
+ Class Balancing
+ Augmentation
0.750 + @ Additional
Conclusion and Acknowledgments

20. Gliding Vertex, New ingenious method of RBOX calculation without angle
could improve the stability and performance effectively.
●
Conclusion and Acknowledgments
This Method is very flexible. This can be applied to other models
(Even applicable to one-stage models such as RetinaNet, EfficientDet)
●
The Important feature of this dataset is that the scale of objects varies greatly.
(Ensemble the model trained with various scale images could improve performance.)
●
Thanks to Dacon & ADD staffs who prepared this competition.●
*Agency for Defense Development
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21. Loss function, Optimizer, Total epochs
→ Faster RCNN Loss fuction, Stochastic Gradient Descent, 17 epochs
●
Question & Answer
The calculation method of obliquity factor
→ RBOX area / HBOX area (The more horizontal, the larger the RBOX Area)
●
Criteria for distinguishing large and small objects
→ 50,000 (Oil-tanker & Container’s Upper Outlier from IQR)
●
Criteria for image split & overlap
→ (1024, 1024), Train (200), Test (512)
●
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