Conveyor Belt Speed Control Based on Object Classes Using a Convolutional Neural Network
DOI:
https://doi.org/10.22153/kej.2026.05.002Keywords:
Conveyor belt system; convolutional neural network (CNN); deep learning (DL); transfer learning: VGG16Abstract
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.
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[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
[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
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