Enhancing Efficiency and Safety with YOLOv5-Powered Robotic Arms for Waste Classification
DOI:
https://doi.org/10.21015/vtse.v12i2.1813Abstract
The world is experiencing a transformation shift from manual labor to digital solutions, making work simpler and more efficient while enhancing the quality of life globally. A prime example of this shift is the Object Picking Robotic Arm (OPRA). Designed to operate with minimal human intervention, the OPRA reduces the risk of physical injuries among workers by replacing human labor with robotic precision. This technology finds applications in both industrial and domestic settings, including the automotive industry, metalworking, chemical processing, and various pick-and-place tasks. In this research, we develop a robotic system for automated waste picking and sorting. This system utilizes the YOLOv5 object detection algorithm to achieve high accuracy (95\%) and precision (90\%) in classifying five common waste categories: cardboard, metal, paper, plastic, and trash.
References
K. E. Clothier and Y. Shang, "A geometric approach for robotic arm kinematics with hardware design, electrical design, and implementation," *Journal of Robotics*, vol. 2010, 2010.
M. A. Rahman, A. H. Khan, T. Ahmed, and M. Sajjad, "Design, analysis and implementation of a robotic arm-the animator," *American Journal of Engineering Research (AJER)*, vol. 2, no. 10, pp. 298–307, 2013.
A. Zade, R. Gautam, A. Gedam, and A. Mahawadiwar, "Review on development of industrial robotic arm," *International Research Journal of Engineering and Technology (IRJET)*, vol. 4, 2017.
"How much does a robotic arm cost?," Last seen on 5 July 2024.
"Recent developments in robotics," Last seen on: 07 July 2024.
Y. Jadeja and B. Pandya, "Design and development of 5-dof robotic arm manipulators," *International Journal Of Scientific & Technology Research*, vol. 8, no. 11, pp. 2158–2167, 2019.
S. Cheng, Y. Jin, and H. Wang, "Deep learning-based control framework for dynamic contact processes in humanoid grasping," *Frontiers in Neurorobotics*, vol. 18, 2024.
J. Palacin, J. A. Salse, I. Valganon, and X. Clua, "Building a mobile robot for a floor-cleaning operation in domestic environments," *IEEE Transactions on Instrumentation and Measurement*, vol. 53, no. 5, pp. 1418–1424, 2004.
G. Sharma, B. Sinha, Pallavi, H. Hakkim, B. P. Chandra, A. Kumar, and V. Sinha, "Gridded emissions of CO, NOx, SO2, CO2, NH3, HCL, CH4, PM2.5, PM10, BC, and NMVOC from open municipal waste burning in India," *Environmental Science & Technology*, vol. 53, no. 9, pp. 4765–4774, 2019.
Y. Demiral, A. Soysal, A. C. Bilgin, B. KiLic, B. Unal, R. UcKu, and T. Theorell, "The association of job strain with coronary heart disease and metabolic syndrome in municipal workers in Turkey," *Journal of Occupational Health*, vol. 48, no. 5, pp. 332–338, 2006.
S. S. Chandra, M. Kulshreshtha, and P. Randhawa, "A review of trash collecting and cleaning robots," in *9th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO)*, pp. 1–5, IEEE, 2021.
W. S. Cheong, S. F. Kamarulzaman, and M. A. Rahman, "Implementation of robot operating system in smart garbage bin robot with obstacle avoidance system," in *Emerging Technology in Computing, Communication and Electronics (ETCCE)*, pp. 1–6, IEEE, 2020.
S. Hossain, B. Debnath, A. Anika, M. Junaed-Al-Hossain, S. Biswas, and C. Shahnaz, "Autonomous trash collector based on object detection using deep neural network," in *2019 IEEE Region 10 Conference (TENCON)*, pp. 1406–1410, IEEE, 2019.
B. Adarsh, H. Shivayogappa, K. Navya, and others, "Automated smart sericulture system based on 6lowpan and image processing technique," in *International Conference on Computer Communication and Informatics (ICCCI)*, pp. 1–6, IEEE, 2016.
R. Rokade, A. Maurya, V. Khade, J. Mali, and others, "Smart garbage separation robot with image processing technique," *International Journal of Engineering Research & Technology (Special Issue)*, 2018.
G. S. Kannan, S. S. Kumar, R. Ragavan, and M. Balakrishnan, "Automatic garbage separation robot using image processing technique," *International Journal of Scientific and Research Publications*, vol. 6, no. 4, pp. 326–328, 2016.
Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, and L. D. Jackel, "Backpropagation applied to handwritten zip code recognition," *Neural Computation*, vol. 1, pp. 541-551, 1989.
M. Sathveek, C. Shreemanth, J. S. Asish, H. T. Agalayil, I. T. B. Mary, and J. J. Paul, "Iot based automatic garbage collection for smart cities," in *6th International Conference on Devices, Circuits and Systems (ICDCS)*, pp. 477–479, IEEE, 2022.
T. Ali, M. Irfan, A. S. Alwadie, and A. Glowacz, "Iot-based smart waste bin monitoring and municipal solid waste management system for smart cities," *Arabian Journal for Science and Engineering*, vol. 45, no. 12, pp. 10185–10198, 2020.
J. Bai, S. Lian, Z. Liu, K. Wang, and D. Liu, "Deep learning based robot for automatically picking up garbage on the grass," *IEEE Transactions on Consumer Electronics*, vol. 64, no. 3, pp. 382–389, 2018.
M. Yang and G. Thung, "Classification of trash for recyclability status," *CS229 Project Report*, 2016.
A. B. Wahyutama and M. Hwang, "Yolo-based object detection for separate collection of recyclables and capacity monitoring of trash bins," *Electronics*, vol. 11, no. 9, p. 1323, 2022.
Y. Arai, R. Miyagusuku, and K. Ozaki, "Development and testing of garbage detection for autonomous robots in outdoor environments," in *IEEE/SICE International Symposium on System Integration (SII)*, pp. 138–143, IEEE, 2021.
M. Tian, X. Li, S. Kong, L. Wu, and J. Yu, "A modified yolov4 detection method for a vision-based underwater garbage cleaning robot," *Frontiers of Information Technology & Electronic Engineering*, vol. 23, no. 8, pp. 1217–1228, 2022.
S. Kong, M. Tian, C. Qiu, Z. Wu, and J. Yu, "Iwscr: An intelligent water surface cleaner robot for collecting floating garbage," *IEEE Transactions on Systems, Man, and Cybernetics: Systems*, vol. 51, no. 10, pp. 6358–6368, 2020.
E. Casas, L. Ramos, E. Bendek, and F. Rivas-Echeverria, "Yolov5 vs. yolov8: Performance benchmarking in wildfire and smoke detection scenarios," *Journal of Image and Graphics*, vol. 12, no. 2, pp. 127–136, 2024.
Y. Liu, B. Lu, J. Peng, and Z. Zhang, "Research on the use of yolov5 object detection algorithm in mask wearing recognition," *World Scientific Research Journal*, vol. 6, no. 11, pp. 276–284, 2020.
S. Butte, A. Vakanski, K. Duellman, H. Wang, and A. Mirkouei, "Potato crop stress identification in aerial images using deep learning-based object detection," *Agronomy Journal*, vol. 113, no. 5, pp. 3991–4002, 2021.
D. W. Wong, A. P. Yow, B. Tan, Y. Xinwen, J. Chua, and L. Schmetterer, "Localization of anatomical features in vascular-enhanced enface oct images," in *42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)*, pp. 1875–1878, IEEE, 2020.
M. Haris and A. Glowacz, "Road object detection: A comparative study of deep learning-based algorithms," *Electronics*, vol. 10, no. 16, p. 1932, 2021.
A. Mejías, R. S. Herrera, M. A. Márquez, A. J. Calderón, I. González, and J. M. Andújar, "Easy handling of sensors and actuators over tcp/ip networks by open source hardware/software," *Sensors*, vol. 17, no. 1, p.
Downloads
Published
How to Cite
Issue
Section
License
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (CC-By) that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
This work is licensed under a Creative Commons Attribution License CC BY
