Marker detection algorithm

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The document describes algorithms for marker detection, board detection, and camera calibration using Aruco. For marker detection, it involves converting to grayscale, thresholding, finding contours, filtering valid markers by checking border and calculating Hamming distance, and computing the marker ID. For board detection, it detects markers, reads intrinsic/board parameters, and calculates extrinsic parameters using solvePnP and projectPoints. For camera calibration, it captures frames with varying distance between, detects markers/board per frame, runs calibrateCamera to update intrinsics, filters outliers, and performs final calibration.

Engineering

Marker Detection Algorithm
(of aruco)
pknam

Aruco Video
• https://www.youtube.com/watch?v=IU7SoMvWNeA
• https://www.youtube.com/watch?v=6F86znm-EMk

Overview
1. Convert to gray
2. cv::adaptiveThreshold
3. cv::findContours
4. Find rectangles
5. Find valid markers
6. Calculate marker’s ID

More details
1. Convert to gray
2. cv::adaptiveThreshold
3. cv::findContours
4. Find rectangles
5. Find valid markers
6. Calculate marker’s ID

2. cv::adaptiveThreshold
thresHold() 내부
blockSize

5. Find valid markers
1. Border check
2. Hamming Distance check

5. Find valid markers (1)
1. warp rectangle
2. threshold
3. slice 7x7
4. check if border is black
- Border check

5. Find valid markers (2)
1 1 1 0 0
0 0 0 1 0
0 0 0 1 0
1 1 1 1 0
0 0 0 0 1
- Hamming Distance check

5. Find valid markers (2)
- Hamming Distance check
{1, 0, 0, 0, 0}
{1, 0, 1, 1, 1}
{0, 1, 0, 0, 1}
{0, 1, 1, 1, 0}
1 1 1 0 0
0 0 0 1 0
0 0 0 1 0
1 1 1 1 0
0 0 0 0 1
Calc lowest hamming distance of each row
Codes
lowest HD
2
2
2
1
1
min(lowest HD) != 0 then invalid

5. Find valid markers (2)
- Hamming Distance check
{1, 0, 0, 0, 0}
{1, 0, 1, 1, 1}
{0, 1, 0, 0, 1}
{0, 1, 1, 1, 0}
0 1 0 0 1
0 1 0 0 1
0 1 0 0 1
0 1 1 1 0
1 0 0 0 0
Codes
Find rotation of which Hamming Distance is 0
lowest HD
0
0
0
0
0
min(lowest HD) == 0 then valid

6. Calculate marker’s ID
0 1 0 0 1
0 1 0 0 1
0 1 0 0 1
0 1 1 1 0
1 0 0 0 0
Marker’s ID == 1010101100(2) == 684(10)

Overview
• Detect markers
• Read camera’s intrinsic parameters from file
• Read board configuration from file
• Calc camera’s extrinsic parameters
– cv::solvePnP()
– cv::projectPoints()
• Draw axis lines (option)
– cv::projectPoints()

Read intrinsic parameters
Created by aruco_calibration.exe
- Camera matrix
- Camera distortion coeff

Read board config
- number of markers
- info of markers (id, corner position)
Created by aruco_create_board.exe

Calc extrinsic parameters (1)
Parameter Description
objPoints corner positions of markers
in board config (3D)
imagePoints corner positions of
detected markers in input
image (2D)
rvec Rotation matrix
tvec Translation matrix
(1) Estimate extrinsic parameters (rvec, tvec)

Calc extrinsic parameters (2)
(2) Refine extrinsic parameters
- Error check with rvec, tvec.
- Exclude point pairs of which projection error is greater than ‘repj_err_thres’
- Re-calculate extrinsic parameters with the filtered pairs
Result!!

Draw axis lines
(0, 0, 0)
(1, 0, 0)
(0, 1, 0)
(0, 0, 1)
Project points onto image

aruco_calibration
Inputs
- Board configuration file (in meters)
- Camera input
Output
- Camera’s intrinsic parameter file

Overview
1. Capture some frames
a. Maintain distance(tvec) between already captured frames
2. Detect markers and board of each frame
a. Get corner points of each marker
b. cv::calibrateCamera() – with current pairs of points
c. Store current pairs of points
d. Update intrinsic parameters
3. cv::calibrateCamera()
4. Filter outliers of points
a. cv::projectPoints()
b. cv::calibrateCamera()

Capture frames
new_tvec
if (new_tvec – old_tvecs) > thres then
capture current frame
old_tvecs.add(new_tvec)
store object_points
store image_points
update intrinsic parameters (cv::calibrateCamera)
end if
old_tvecs
- For various point of view

Calc intrinsic parameters
- Calculate intrinsic parameters with stored data
Result

Filtering outliers
- Exclude outliers of which projection error is greater than A VALUE

Re-calibration
- Than, calc intrinsic parameters again, the FINAL result.

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The smart irrigation system represents an innovative approach to optimize water usage in agricultural and landscaping practices. The integration of cutting-edge technologies, including sensors, actuators, and data analysis, empowers this system to provide accurate monitoring and control of irrigation processes by leveraging real-time environmental conditions. The main objective of a smart irrigation system is to optimize water efficiency, minimize expenses, and foster the adoption of sustainable water management methods. This paper conducts a systematic risk assessment by exploring the key components/assets and their functionalities in the smart irrigation system. The crucial role of sensors in gathering data on soil moisture, weather patterns, and plant well-being is emphasized in this system. These sensors enable intelligent decision-making in irrigation scheduling and water distribution, leading to enhanced water efficiency and sustainable water management practices. Actuators enable automated control of irrigation devices, ensuring precise and targeted water delivery to plants. Additionally, the paper addresses the potential threat and vulnerabilities associated with smart irrigation systems. It discusses limitations of the system, such as power constraints and computational capabilities, and calculates the potential security risks. The paper suggests possible risk treatment methods for effective secure system operation. In conclusion, the paper emphasizes the significant benefits of implementing smart irrigation systems, including improved water conservation, increased crop yield, and reduced environmental impact. Additionally, based on the security analysis conducted, the paper recommends the implementation of countermeasures and security approaches to address vulnerabilities and ensure the integrity and reliability of the system. By incorporating these measures, smart irrigation technology can revolutionize water management practices in agriculture, promoting sustainability, resource efficiency, and safeguarding against potential security threats.

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As digital technology becomes more deeply embedded in power systems, protecting the communication networks of Smart Grids (SG) has emerged as a critical concern. Distributed Network Protocol 3 (DNP3) represents a multi-tiered application layer protocol extensively utilized in Supervisory Control and Data Acquisition (SCADA)-based smart grids to facilitate real-time data gathering and control functionalities. Robust Intrusion Detection Systems (IDS) are necessary for early threat detection and mitigation because of the interconnection of these networks, which makes them vulnerable to a variety of cyberattacks. To solve this issue, this paper develops a hybrid Deep Learning (DL) model specifically designed for intrusion detection in smart grids. The proposed approach is a combination of the Convolutional Neural Network (CNN) and the Long-Short-Term Memory algorithms (LSTM). We employed a recent intrusion detection dataset (DNP3), which focuses on unauthorized commands and Denial of Service (DoS) cyberattacks, to train and test our model. The results of our experiments show that our CNN-LSTM method is much better at finding smart grid intrusions than other deep learning algorithms used for classification. In addition, our proposed approach improves accuracy, precision, recall, and F1 score, achieving a high detection accuracy rate of 99.50%.

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Marker detection algorithm

1. Marker Detection Algorithm (of aruco) pknam

2. Aruco Video • https://www.youtube.com/watch?v=IU7SoMvWNeA • https://www.youtube.com/watch?v=6F86znm-EMk

3. Overview 1. Convert to gray 2. cv::adaptiveThreshold 3. cv::findContours 4. Find rectangles 5. Find valid markers 6. Calculate marker’s ID

4. Test Image

5. 1. Convert to gray

6. 2. threshold

7. 3. find contours

8. find rectangles

9. if(rectangle is marker)

10. one of the markers

11. calc marker ID

12. More details 1. Convert to gray 2. cv::adaptiveThreshold 3. cv::findContours 4. Find rectangles 5. Find valid markers 6. Calculate marker’s ID

13. 2. cv::adaptiveThreshold thresHold() 내부 blockSize

14. 5. Find valid markers 1. Border check 2. Hamming Distance check

15. 5. Find valid markers (1) 1. warp rectangle 2. threshold 3. slice 7x7 4. check if border is black - Border check

16. 5. Find valid markers (1) 1. warp rectangle 2. threshold 3. slice 7x7 4. check if border is black - Border check

17. 5. Find valid markers (2) 1 1 1 0 0 0 0 0 1 0 0 0 0 1 0 1 1 1 1 0 0 0 0 0 1 - Hamming Distance check

18. 5. Find valid markers (2) - Hamming Distance check {1, 0, 0, 0, 0} {1, 0, 1, 1, 1} {0, 1, 0, 0, 1} {0, 1, 1, 1, 0} 1 1 1 0 0 0 0 0 1 0 0 0 0 1 0 1 1 1 1 0 0 0 0 0 1 Calc lowest hamming distance of each row Codes lowest HD 2 2 2 1 1 min(lowest HD) != 0 then invalid

19. 5. Find valid markers (2) - Hamming Distance check {1, 0, 0, 0, 0} {1, 0, 1, 1, 1} {0, 1, 0, 0, 1} {0, 1, 1, 1, 0} 0 1 0 0 1 0 1 0 0 1 0 1 0 0 1 0 1 1 1 0 1 0 0 0 0 Codes Find rotation of which Hamming Distance is 0 lowest HD 0 0 0 0 0 min(lowest HD) == 0 then valid

20. 6. Calculate marker’s ID 0 1 0 0 1 0 1 0 0 1 0 1 0 0 1 0 1 1 1 0 1 0 0 0 0 Marker’s ID == 1010101100(2) == 684(10)

21. Board Detection Algorithm (of Aruco)

22. Overview • Detect markers • Read camera’s intrinsic parameters from file • Read board configuration from file • Calc camera’s extrinsic parameters – cv::solvePnP() – cv::projectPoints() • Draw axis lines (option) – cv::projectPoints()

23. Detect markers

24. Read intrinsic parameters Created by aruco_calibration.exe - Camera matrix - Camera distortion coeff

25. Read board config - number of markers - info of markers (id, corner position) Created by aruco_create_board.exe

26. Calc extrinsic parameters (1) Parameter Description objPoints corner positions of markers in board config (3D) imagePoints corner positions of detected markers in input image (2D) rvec Rotation matrix tvec Translation matrix (1) Estimate extrinsic parameters (rvec, tvec)

27. Calc extrinsic parameters (2) (2) Refine extrinsic parameters - Error check with rvec, tvec. - Exclude point pairs of which projection error is greater than ‘repj_err_thres’ - Re-calculate extrinsic parameters with the filtered pairs Result!!

28. Draw axis lines (0, 0, 0) (1, 0, 0) (0, 1, 0) (0, 0, 1) Project points onto image

29. Camera Calibration Algorithm (of Aruco)

30. aruco_calibration Inputs - Board configuration file (in meters) - Camera input Output - Camera’s intrinsic parameter file

31. Overview 1. Capture some frames a. Maintain distance(tvec) between already captured frames 2. Detect markers and board of each frame a. Get corner points of each marker b. cv::calibrateCamera() – with current pairs of points c. Store current pairs of points d. Update intrinsic parameters 3. cv::calibrateCamera() 4. Filter outliers of points a. cv::projectPoints() b. cv::calibrateCamera()

32. Capture frames new_tvec if (new_tvec – old_tvecs) > thres then capture current frame old_tvecs.add(new_tvec) store object_points store image_points update intrinsic parameters (cv::calibrateCamera) end if old_tvecs - For various point of view

33. Calc intrinsic parameters - Calculate intrinsic parameters with stored data Result

34. Filtering outliers - Exclude outliers of which projection error is greater than A VALUE

35. Re-calibration - Than, calc intrinsic parameters again, the FINAL result.

Marker detection algorithm

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