Industry 4.0: Hand Recognition on Assembly Supervision Process

  • Riska Analia Politeknik Negeri Batam
  • Andika Putra Pratama Politeknik Negeri Batam
  • Susanto Susanto Politeknik Negeri Batam
Keywords: Industry 4.0, real-time hand detection, real-time supervision assembly process


In the assembly industry, the process of assembling components is very important in order to produce a quality product. Assembly of components should be carried out sequentially based on the standards set by the company. For companies that still operate the assembly process manually by employee, sometimes errors occur in the assembly process, which can affect the quality of production. In order to be carried out the assembly process according to the procedure, a system is needed that can detect employee hands when carrying out the assembly process automatically. This study proposes an artificial intelligence-based real-time employee hand detection system. This system will be the basis for the development of an automatic industrial product assembly process to welcome the Industry 4.0. To verify system performance, several experiments were carried out, such as; detecting the right and left hands of employees and detecting hands when using accessories or not. From the experimental results it can be concluded that the system is able to detect the right and left hands of employees well with the resulting FPS average of 15.4.


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S. Gulati and R. K. Bhogal, "Comprehensive Review of various Hand Detection Approaches," 2018 International Conference on Circuits and Systems in Digital Enterprise Technology (ICCSDET), Kottayam, India, 2018, pp. 1-5, doi: 10.1109/ICCSDET.2018.8821238.

M. Bousaaid, T. Ayaou, K. Afdel and P. Estraillier, "Hand gesture detection and recognition in cyber presence interactive system for E-learning," 2014 International Conference on Multimedia Computing and Systems (ICMCS), Marrakech, 2014, pp. 444-447, doi: 10.1109/ICMCS.2014.6911197.

J. Lin, F. Jiang and R. Shen, "Hand-Raising Gesture Detection in Real Classroom," 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Calgary, AB, 2018, pp. 6453-6457, doi: 10.1109/ICASSP.2018.8461733.

Q. Gao, J. Liu, Z. Ju and X. Zhang, "Dual-Hand Detection for Human–Robot Interaction by a Parallel Network Based on Hand Detection and Body Pose Estimation," in IEEE Transactions on Industrial Electronics, vol. 66, no. 12, pp. 9663-9672, Dec. 2019, doi: 10.1109/TIE.2019.2898624.

T. Grzejszczak, A. Łegowski and M. Niezabitowski, "Application of hand detection algorithm in robot control," 2016 17th International Carpathian Control Conference (ICCC), Tatranska Lomnica, 2016, pp. 222-225, doi: 10.1109/CarpathianCC.2016.7501098.

X. Yu, L. Zhu and L. Jia, "Detection and recognition of hand abnormal state based on deep learning algorithm," 2020 IEEE International Conference on Artificial Intelligence and Information Systems (ICAIIS), Dalian, China, 2020, pp. 190-193, doi: 10.1109/ICAIIS49377.2020.9194919.

M. -F. Tsai, R. H. Wang and J. Zariffa, "Generalizability of Hand-Object Interaction Detection in Egocentric Video across Populations with Hand Impairment," 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Montreal, QC, Canada, 2020, pp. 3228-3231, doi: 10.1109/EMBC44109.2020.9176154.

F. Naim, R. Jaafar, N. W. Arshad, R. Hamid and M. N. Razali, "Unclean hand detection machine using vision sensor," 2013 Saudi International Electronics, Communications and Photonics Conference, Fira, 2013, pp. 1-4, doi: 10.1109/SIECPC.2013.6550750.

R. Sharma, R. Shikher, N. V. Bansode and P. R. Rajarapollu, "Interactive projector screen with hand detection using gestures," 2016 International Conference on Automatic Control and Dynamic Optimization Techniques (ICACDOT), Pune, 2016, pp. 574-577, doi: 10.1109/ICACDOT.2016.7877650.

Z. Yi, X. Hu, B. Jang and K. K. Kim, "A robust and parallel-friendly distance image based hand detection," 2015 International SoC Design Conference (ISOCC), Gyungju, 2015, pp. 33-34, doi: 10.1109/ISOCC.2015.7401651.

M. Ç. Uysal, T. Karapinar, B. Benligiray and C. Topal, "Dataset augmentation for accurate object detection," 2018 26th Signal Processing and Communications Applications Conference (SIU), Izmir, 2018, pp. 1-4, doi: 10.1109/SIU.2018.8404807.

A. Gupta, Y. Kumar and S. Malhotra, "Banking security system using hand gesture recognition," 2015 International Conference on Recent Developments in Control, Automation and Power Engineering (RDCAPE), Noida, 2015, pp. 243-246, doi: 10.1109/RDCAPE.2015.7281403.

Y. Wang, W. Lin and L. Yang, "A fast hand motion detection based on FIFO," 2017 International Conference on Machine Learning and Cybernetics (ICMLC), Ningbo, 2017, pp. 599-604, doi: 10.1109/ICMLC.2017.8108978.

J. Kim, J. Baek and E. Kim, "A part-based rotational invariant hand detection," 2013 International Conference on Fuzzy Theory and Its Applications (iFUZZY), Taipei, 2013, pp. 127-129, doi: 10.1109/iFuzzy.2013.6825422.

S. Han, S. Park, M. Kim and J. Paik, "Hand detection using facial information," 2016 IEEE 6th International Conference on Consumer Electronics - Berlin (ICCE-Berlin), Berlin, 2016, pp. 167-168, doi: 10.1109/ICCE-Berlin.2016.7684746.

Y. Zheng and P. Zheng, "Hand Contour Detection Using Spatial Homomorphic Filtering and Variational Level Set," 2015 International Conference on Computer Science and Applications (CSA), Wuhan, 2015, pp. 172-176, doi: 10.1109/CSA.2015.15.

K. Yao, S. Lan, H. Tang, Z. He and C. Yang, "A 24GHz Micropatch Antenna Array for Human Hand Gestures Detection," 2018 USNC-URSI Radio Science Meeting (Joint with AP-S Symposium), Boston, MA, 2018, pp. 95-96, doi: 10.1109/USNC-URSI.2018.8602899.

P. N. Crisnapati, M. Setiawan, I. G. N. Wikranta Arsa, P. Devi Novayanti, M. S. Wibawa and K. G. Oka Ciptahadi, "Real-Time Hand Palm Detection and Tracking Augmented Reality Game Using Lucas Kanade Optical Flow Combined with Color Blob Detection," 2019 1st International Conference on Cybernetics and Intelligent System (ICORIS), Denpasar, Bali, Indonesia, 2019, pp. 263-268, doi: 10.1109/ICORIS.2019.8874892.

J. Y. Oh, J. Lee, J. H. Lee and J. H. Park, "A hand and wrist detection method for unobtrusive hand gesture interactions using HMD," 2016 IEEE International Conference on Consumer Electronics-Asia (ICCE-Asia), Seoul, 2016, pp. 1-4, doi: 10.1109/ICCE-Asia.2016.7804805.

Y. Sun, X. Liang, H. Fan, M. Imran and H. Heidari, "Visual Hand Tracking on Depth Image using 2-D Matched Filter," 2019 UK/ China Emerging Technologies (UCET), Glasgow, United Kingdom, 2019, pp. 1-4, doi: 10.1109/UCET.2019.8881866.

C. Li and K. M. Kitani, "Pixel-Level Hand Detection in Ego-centric Videos," 2013 IEEE Conference on Computer Vision and Pattern Recognition, Portland, OR, 2013, pp. 3570-3577, doi: 10.1109/CVPR.2013.458.

S. Misra and R. H. Laskar, "Taxonomy of Texture and Color-Texture Features for Developing Hand Detection System under Non-Ideal Conditions," 2017 14th IEEE India Council International Conference (INDICON), Roorkee, 2017, pp. 1-6, doi: 10.1109/INDICON.2017.8487569.

R. Adiguna and Y. E. Soelistio, "CNN Based Posture-Free Hand Detection," 2018 10th International Conference on Information Technology and Electrical Engineering (ICITEE), Kuta, 2018, pp. 276-279, doi: 10.1109/ICITEED.2018.8534743.

Joseph Redmon, Santosh Divvala, Ross Girshick, Ali Farhadi, “You Only Look Once: Unifield, Real-Time Object Detection,” unpublished, 2016.

Susanto, E. Rudiawan, R. Analia, P. Daniel Sutopo and H. Soebakti, "The deep learning development for real-time ball and goal detection of barelang-FC," 2017 International Electronics Symposium on Engineering Technology and Applications (IES-ETA), Surabaya, 2017, pp. 146-151, doi: 10.1109/ELECSYM.2017.8240393.

C. Liu, Y. Tao, J. Liang, K. Li and Y. Chen, "Object Detection Based on YOLO Network," 2018 IEEE 4th Information Technology and Mechatronics Engineering Conference (ITOEC), Chongqing, China, 2018, pp. 799-803, doi: 10.1109/ITOEC.2018.8740604.

W. Yang and Z. Jiachun, "Real-time face detection based on YOLO," 2018 1st IEEE International Conference on Knowledge Innovation and Invention (ICKII), Jeju, 2018, pp. 221-224, doi: 10.1109/ICKII.2018.8569109.

D. Garg, P. Goel, S. Pandya, A. Ganatra and K. Kotecha, "A Deep Learning Approach for Face Detection using YOLO," 2018 IEEE Punecon, Pune, India, 2018, pp. 1-4, doi: 10.1109/PUNECON.2018.8745376.

C. Zhao and B. Chen, "Real-Time Pedestrian Detection Based on Improved YOLO Model," 2019 11th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC), Hangzhou, China, 2019, pp. 25-28, doi: 10.1109/IHMSC.2019.10101.

T. Le, D. Jaw, I. Lin, H. Liu and S. Huang, "An efficient hand detection method based on convolutional neural network," 2018 7th International Symposium on Next Generation Electronics (ISNE), Taipei, 2018, pp. 1-2, doi: 10.1109/ISNE.2018.8394651.

Joseph Redmon, & Ali Farhadi. (2018). YOLOv3: An Incremental Improvement.

J. Hu, X. Gao, H. Wu and S. Gao, "Detection of Workers Without the Helments in Videos Based on YOLO V3," 2019 12th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), Suzhou, China, 2019, pp. 1-4, doi: 10.1109/CISP-BMEI48845.2019.8966045.