Volume 2, Issue 1, No.1 PDF DOWNLOAD
  • Title:
  • Object size measurement and camera distance evaluation for electronic components using Fixed-Position camera
  • Author:

    Minh Long Hoang

  • Author Affiliation:

    Department of Engineering and Architecture, University of Parma, Parma, Italy

  • Received:Feb.17, 2023
  • Accepted:Mar.2, 2023
  • Published:Mar.20, 2023
This article works on applying Open-Source Computer Vision Library (OpenCV) minimum area rectangle to measure electronics component dimension. A rotative contour covers the considered objects with the detected width and length. The pixel and real-world unit ratio are identified with a reference object for other device size accomplishment. The experiment contains Arduino UNO, microchip ESP32-WROOM, Inertial Measurement Unit (IMU) sensor, and a 9 V battery. The approach shows the less complicated way to achieve the appropriate results, with an absolute error of less than 3mm. The distance between the camera and object is also calculated based on the relationship between camera parameters and actual object height. The research concentrates on the size measurement of electronic components and the distance estimation from the object to the monitoring camera.

Computer vision, size measurement, electronics, camera distance evaluation.


[1] V. Lepetit, "On Computer Vision for Augmented Reality," 2008 International Symposium on Ubiquitous Virtual Reality, Gwangju, Korea (South), 2008, pp. 13-16, doi: 10.1109/ISUVR.2008.10.

[2] V. Wiley and T. Lucas, "Computer Vision and image processing: A paper review," International Journal of Artificial Intelligence Research, vol. 2, no. 1, p. 22, 2018.

[3] S. S. Ankad, B. Shivaprasad Raj, A. Nasreen, S. Mathur, P. Ramakanth Kumar and K. Sreelakshmi, "Object Size Measurement from CCTV footage using deep learning," 2021 IEEE International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS), Bangalore, India, 2021, pp. 1-5, doi: 

[4] M. L. Hoang and A. Pietrosanto, "New Artificial Intelligence Approach to Inclination Measurement Based on MEMS Accelerometer," in IEEE Transactions on Artificial Intelligence, vol. 3, no. 1, pp. 67-77, Feb. 2022, doi: 10.1109/TAI.2021.3105494. 10.1109/CSITSS54238.2021.9683671.

[5] X. Yu, T. W. Kuan, Y. Zhang and T. Yan, "YOLO v5 for SDSB Distant Tiny Object Detection," 2022 10th International Conference on Orange Technology (ICOT), Shanghai, China, 2022, pp. 1-4, doi: 10.1109/ICOT56925.2022.10008164.

[6] R. Xin, J. Zhang and Y. Shao, "Complex network classification with convolutional neural network," in Tsinghua Science and Technology, vol. 25, no. 4, pp. 447-457, Aug. 2020, doi: 10.26599/TST.2019.9010055.

[7] A. Zelinsky, "Learning opencv---computer vision with the opencv library (Bradski, G.R. et al.; 2008) [on the shelf]," IEEE Robotics & Automation Magazine, vol. 16, no. 3, pp. 100–100, 2009. 

[8] M. L. Hoang, A. A. Nkembi, and P. L. Pham, "Real-time risk assessment detection for weak people by parallel training logical execution of a supervised learning system based on an IOT wearable MEMS accelerometer," Sensors, vol. 23, no. 3, p. 1516, 2023.

[9] M. L. Hoang and A. Pietrosanto, "A New Technique on Vibration Optimization of Industrial Inclinometer for MEMS Accelerometer Without Sensor Fusion," in IEEE Access, vol. 9, pp. 20295-20304, 2021, doi: 10.1109/ACCESS.2021.3054825.

[10] Wayne Fulton, "Www.scantips.com," Calculator to compute the Distance or Size of an Object in a photo Image. [Online]. Available: https://www.scantips.com/lights/subjectdistance.html. [Accessed: 16-Feb-2023].

[11] T. A. Team, "Uno R3: Arduino documentation," Arduino Documentation | Arduino Documentation. [Online]. Available: https://www.arduino.cc/en/Main/ArduinoBoardUno. [Accessed: 16-Feb-2023].

[12] MPU-6000/MPU-6050 Product Specification Document Number: PSMPU-6000A-00, August, 2013.

[13] M. L. Hoang, M. Carratù, V. Paciello and A. Pietrosanto, "Noise Attenuation on IMU Measurement for Drone Balance by Sensor Fusion," 2021 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), Glasgow, United Kingdom, 2021, pp. 1-6, doi: 10.1109/I2MTC50364.2021.9460041.

[14] Espressif Systems, ESP32-WROOM-32 Datasheet, 2022. 

[15] M. Mahendru and S. K. Dubey, "Real Time Object Detection with Audio Feedback using Yolo vs. Yolo_v3," 2021 11th International Conference on Cloud Computing, Data Science & Engineering (Confluence), Noida, India, 2021, pp. 734-740, doi: 10.1109/Confluence51648.2021.9377064.

[16] Y. -S. Poon, C. -C. Lin, Y. -H. Liu and C. -P. Fan, "YOLO-Based Deep Learning Design for In-Cabin Monitoring System with Fisheye-Lens Camera," 2022 IEEE International Conference on Consumer Electronics (ICCE), Las Vegas, NV, USA, 2022, pp. 1-4, doi: 10.1109/ICCE53296.2022.9730235.

[17] X. Wang, X. Jiang, Z. Xia and X. Feng, "Underwater Object Detection Based on Enhanced YOLO," 2022 International Conference on Image Processing and Media Computing (ICIPMC), Xi'an, China, 2022, pp. 17-21, doi: 10.1109/ICIPMC55686.2022.00012.

[18] X. Wang, X. Jiang, Z. Xia and X. Feng, "Underwater Object Detection Based on Enhanced YOLO," 2022 International Conference on Image Processing and Media Computing (ICIPMC), Xi'an, China, 2022, pp. 17-21, doi: 10.1109/ICIPMC55686.2022.00012.

[19] F. A. Uçkun, H. Özer, E. Nurbaş and E. Onat, "Direction Finding Using Convolutional Neural Networks and Convolutional Recurrent Neural Networks," 2020 28th Signal Processing and Communications Applications Conference (SIU), Gaziantep, Turkey, 2020, pp. 1-4, doi: 10.1109/SIU49456.2020.9302448.

[20] M. Bersali, A. Rachedi and H. Bouarfa, "Convolutional neural network for relays selection in the Internet of Vehicles," 2021 International Conference on Forthcoming Networks and Sustainability in AIoT Era (FoNeS-AIoT), Nicosia, Turkey, 2021, pp. 57-61, doi: 10.1109/FoNeS-AIoT54873.2021.00022.

[21] P. Samudre, P. Shende and V. Jaiswal, "Optimizing Performance of Convolutional Neural Network Using Computing Technique," 2019 IEEE 5th International Conference for Convergence in Technology (I2CT), Bombay, India, 2019, pp. 1-4, doi: 10.1109/I2CT45611.2019.9033876.

[22] M. L. Hoang and A. Pietrosanto, "Yaw/Heading optimization by drift elimination on MEMS gyroscope," Sensors and Actuators A: Physical, vol. 325, p. 112691, Jul. 2021, doi: https://doi.org/10.1016/j.sna.2021.112691.

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