Conveyor Belt Speed Control Based on Object Classes Using a Convolutional Neural Network

Murtadha Shukur Mahmood; Israa Rafie Shareef; Furat  I. Hussein; Muhannad  Ahmed Obeidi

doi:10.22153/kej.2026.05.002

Authors

Murtadha Shukur Mahmood Department of Mechatronics Engineering, Al-Khawarizmi College of Engineering, University of Baghdad, Baghdad, Iraq
Israa Rafie Shareef Department of Mechatronics Engineering, Al-Khawarizmi College of Engineering, University of Baghdad, Baghdad, Iraq
Furat I. Hussein Department of Mechatronics Engineering, Al-Khawarizmi College of Engineering, University of Baghdad, Baghdad, Iraq
Muhannad Ahmed Obeidi School of Mechanical Engineering, Technological University Dublin, Ireland

DOI:

https://doi.org/10.22153/kej.2026.05.002

Keywords:

Conveyor belt system; convolutional neural network (CNN); deep learning (DL); transfer learning: VGG16

Abstract

Conveyor belt systems facilitate the movement of goods and materials, making them an essential part of various fields. Deep learning control methods are therefore indispensable to assure their energy efficiency and optimal motor speed. Here, training is conducted via a convolutional neural network with transfer learning using VGG16 base net. Feature extraction layers are fixed, whilst fully connected layers are replaced. Thirty-three appropriate object classes are considered for classification, and images are acquired using an overhead camera. The obtained weight values are transmitted to Arduino that uses these data to control the stepper motor speed for optimal performance and minimum energy consumption, achieving approximate estimates of weight and efficient control of the motor. This work integrates the following hardware components: an Arduino microcontroller, a NEMA17 stepper motor, a GY-219 digital current sensor (INA219), an L298N motor driver and a high-resolution camera that processes images using Python with TensorFlow and OpenCV. These components enable the conveyor belt system to realise precise classification and instantaneous regulation of objects. Experimental results show the effectiveness of the proposed system in energising and managing the conveyor belt with an accuracy of 99.47% and a loss of 0.04874, highlighting how we can use the developed system to maximise energy economy and classification accuracy. For academics, engineers and business leaders, this study provides important new perspectives on the current scene and expected future developments in this expanding field.

Downloads

Download data is not yet available.

References

[1] Y. Liu, C. Miao, X. Li, and G. Xu, "Research on deviation detection of belt conveyor based on inspection robot and deep learning," Complexity, vol. 2021, Article ID 3734560, 2021. https://doi.org/10.1155/2021/3734560 DOI: https://doi.org/10.1155/2021/3734560

[2] M. Zhang, Y. Zhang, Y. Wang, and H. Wang, "Application of lightweight convolutional neural network for damage detection of conveyor belt," Applied Sciences, vol. 11, no. 16, pp. 1–15, 2021. https://doi.org/10.3390/app11167282 DOI: https://doi.org/10.3390/app11167282

[3] I. R. Shareef and H. K. Hussein, "Implementation of artificial neural network to achieve speed control and power saving of a belt conveyor system," Eastern-European Journal of Enterprise Technologies, vol. 2, no. 2(110), pp. 6–13, 2021. https://doi.org/10.15587/1729-4061.2021.224137 DOI: https://doi.org/10.15587/1729-4061.2021.224137

[4] N. M. Sulaiman, A. Al Tmeme, and M. N. Mahdi, "An overview of audio-visual source separation using deep learning," Al-Khwarizmi Engineering Journal, vol. 19, no. 4, pp. 42–55, 2023. https://doi.org/10.22153/kej.2023.06.003 DOI: https://doi.org/10.22153/kej.2023.06.003

[5] A. H. M. Yahya Ghufran Khidhir, "Real-time end-to-end self-driving car navigation," International Journal of Intelligent Systems and Applications in Engineering, vol. 11, no. 2S, pp. 366–372, 2023. https://doi.org/10.18201/ijisae.2732

[6] D. Varna and V. Abromavičius, "A system for a real-time electronic component detection and classification on a conveyor belt," Applied Sciences, vol. 12, no. 11, pp. 1–13, 2022. https://doi.org/10.3390/app12115608 DOI: https://doi.org/10.3390/app12115608

[7] P. Bortnowski, H. Gondek, R. Król, D. Marasova, and M. Ozdoba, "Detection of blockages of the belt conveyor transfer point using an RGB camera and CNN autoencoder," Energies, vol. 16, no. 4, pp. 1–15, 2023. https://doi.org/10.3390/en16041666 DOI: https://doi.org/10.3390/en16041666

[8] S. A. Althubiti, F. Alenezi, S. Shitharth, S. K, and C. V. S. Reddy, "Circuit manufacturing defect detection using VGG16 convolutional neural networks," Wireless Communications and Mobile Computing, vol. 2022, Article ID 1070405, 2022. https://doi.org/10.1155/2022/1070405 DOI: https://doi.org/10.1155/2022/1070405

[9] A. A. M. S. Ibrahim and J. R. Tapamo, "Transfer learning-based approach using new convolutional neural network classifier for steel surface defects classification," Scientific African, vol. 23, p. e02066, 2024. https://doi.org/10.1016/j.sciaf.2024.e02066 DOI: https://doi.org/10.1016/j.sciaf.2024.e02066

[10] M. Shaha and M. Pawar, "Transfer learning for image classification," in Proc. 2nd Int. Conf. Electronics, Communication and Aerospace Technology (ICECA), Coimbatore, India, pp. 656–660, 2018. https://doi.org/10.1109/ICECA.2018.8474802 DOI: https://doi.org/10.1109/ICECA.2018.8474802

[11] D. I. Swasono, H. Tjandrasa, and C. Fathicah, "Classification of tobacco leaf pests using VGG16 transfer learning," in Proc. 12th Int. Conf. Information & Communication Technology and System (ICTS), Surabaya, Indonesia, pp. 176–181, 2019. https://doi.org/10.1109/ICTS.2019.8850946 DOI: https://doi.org/10.1109/ICTS.2019.8850946

[12] A. A. Santos, F. A. S. Rocha, A. J. da R. Reis, and F. G. Guimarães, "Automatic system for visual detection of dirt buildup on conveyor belts using convolutional neural networks," Sensors, vol. 20, no. 20, pp. 1–16, 2020. https://doi.org/10.3390/s20205762 DOI: https://doi.org/10.3390/s20205762

[13] S. Tammina, "Transfer learning using VGG-16 with deep convolutional neural network for classifying images," International Journal of Scientific and Research Publications (IJSRP), vol. 9, no. 10, pp. 143–150, 2019. https://doi.org/10.29322/IJSRP.9.10.2019.p9420 DOI: https://doi.org/10.29322/IJSRP.9.10.2019.p9420

[14] M. Alencastre-Miranda, R. M. Johnson, and H. I. Krebs, "Convolutional neural networks and transfer learning for quality inspection of different sugarcane varieties," IEEE Transactions on Industrial Informatics, vol. 17, no. 2, pp. 787–794, 2021. https://doi.org/10.1109/TII.2020.2997474 DOI: https://doi.org/10.1109/TII.2020.2992229

[15] E. Noh and S. Hong, "Automatic screening of bolts with anti-loosening coating using grad-CAM and transfer learning with deep convolutional neural networks," Applied Sciences, vol. 12, no. 4, pp. 1–11, 2022. https://doi.org/10.3390/app12042029 DOI: https://doi.org/10.3390/app12042029

[16] T. Kaur and T. K. Gandhi, "Automated brain image classification based on VGG-16 and transfer learning," in Proc. Int. Conf. Information Technology (ICIT), Bhubaneswar, India, pp. 94–98, 2019. https://doi.org/10.1109/ICIT48102.2019.00023 DOI: https://doi.org/10.1109/ICIT48102.2019.00023

[17] Y. G. Khidhir and A. H. Morad, "Comparative transfer learning models for end-to-end self-driving car," Al-Khwarizmi Engineering Journal, vol. 18, no. 4, pp. 45–59, 2022. https://doi.org/10.22153/kej.2022.09.003 DOI: https://doi.org/10.22153/kej.2022.09.003

[18] Z. Shi, Y. Zhang, Y. Zhang, and J. Zhang, "A deep CNN based transfer learning method for false positive reduction," Multimedia Tools and Applications, vol. 78, pp. 1017–1033, 2019. https://doi.org/10.1007/s11042-017-5370-6 DOI: https://doi.org/10.1007/s11042-018-6082-6

[19] M. Hussain, J. J. Bird, and D. R. Faria, "A study on CNN transfer learning for image classification," in Advances in Computational Intelligence Systems, vol. 840, pp. 191–202, 2019. https://doi.org/10.1007/978-3-030-04239-4_15 DOI: https://doi.org/10.1007/978-3-319-97982-3_16