Vision-based dirt distribution mapping using deep learning | Scientific Reports – Nature.com

Faremi, F. A., Ogunfowokan, A. A., Olatubi, M. I., Ogunlade, B. & Ajayi, O. A. Knowledge of occupational hazards among cleaning workers: A study of cleaners of a Nigerian university. Int. J. Health Sci. Res. 4, 198204 (2014).

Google Scholar

Lin, J.-H. et al. Cleaning in the 21st century: The musculoskeletal disorders associated with the centuries-old occupationa literature review. Appl. Ergon. 105, 103839. https://doi.org/10.1016/j.apergo.2022.103839 (2022).

Article PubMed Google Scholar

Elkmann, N., Hortig, J. & Fritzsche, M. Cleaning automation, 12531264 (Springer, 2009).

MATH Google Scholar

Samarakoon, S. B. P., Muthugala, M. V. J., Le, A. V. & Elara, M. R. Htetro-infi: A reconfigurable floor cleaning robot with infinite morphologies. IEEE Access 8, 6981669828 (2020).

Article Google Scholar

Bisht, R. S., Pathak, P. M. & Panigrahi, S. K. Design and development of a glass faade cleaning robot. Mech. Mach. Theory 168, 104585. https://doi.org/10.1016/j.mechmachtheory.2021.104585 (2022).

Article Google Scholar

Batista, V. R. & Zampirolli, F. A. Optimising robotic pool-cleaning with a genetic algorithm. J. Intell. Robot. Syst. 95, 443458. https://doi.org/10.1007/s10846-018-0953-y (2019).

Article Google Scholar

Yamanaka, Y., Hitomi, T., Ito, F. & Nakamura, T. Evaluation of optimal cleaning tools for the development of a cleaning robot for grease from ventilation ducts. In Robotics for sustainable future (eds Chugo, D. et al.) 348356 (Springer, 2022).

Chapter Google Scholar

Muthugala, M. V. J., Samarakoon, S. B. P., Veerajagadheswar, P. & Elara, M. R. Ensuring area coverage and safety of a reconfigurable staircase cleaning robot. IEEE Access 9, 150049150059 (2021).

Article Google Scholar

CAGR of 22.7%, cleaning robot market size to hit usd 34.94 billion in 2028, says brandessence market research, accessed 24 March 2022); https://www.prnewswire.com/news-releases/cagr-of-22-7-cleaning-robot-market-size-to-hit-usd-34-94-billion-in-2028--says-brandessence-market-research-301509925.html.

Samarakoon, S. M. B. P., Muthugala, M. A. V. J. & Elara, M. R. Online complete coverage path planning of a reconfigurable robot using glasius bio-inspired neural network and genetic algorithm. In 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 57445751 (IEEE, 2022).

Muthugala, M. V. J., Samarakoon, S. B. P. & Elara, M. R. Tradeoff between area coverage and energy usage of a self-reconfigurable floor cleaning robot based on user preference. IEEE Access 8, 7626776275 (2020).

Article Google Scholar

Samarakoon, S. M. B. P., Muthugala, M. A. V. J., Kalimuthu, M., Chandrasekaran, S. K. & Elara, M. R. Design of a reconfigurable robot with size-adaptive path planner. In 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 157164 (IEEE, 2022).

Prassler, E., Ritter, A., Schaeffer, C. & Fiorini, P. A short history of cleaning robots. Auton. Robots 9, 211226 (2000).

Article Google Scholar

Yapici, N.B., Tuglulular, T. & Basoglu, N. Assessment of human-robot interaction between householders and robotic vacuum cleaners. In 2022 IEEE Technology and Engineering Management Conference (TEMSCON EUROPE), 204209 (IEEE, 2022).

Rizk, Y., Awad, M. & Tunstel, E. W. Cooperative heterogeneous multi-robot systems: A survey. ACM Comput. Surv. (CSUR) 52, 131 (2019).

Article Google Scholar

Ramalingam, B. et al. Optimal selective floor cleaning using deep learning algorithms and reconfigurable robot htetro. Sci. Rep. 12, 15938. https://doi.org/10.1038/s41598-022-19249-7 (2022).

Article ADS CAS PubMed PubMed Central Google Scholar

Cebollada, S., Pay, L., Flores, M., Peidr, A. & Reinoso, O. A state-of-the-art review on mobile robotics tasks using artificial intelligence and visual data. Expert Syst. Appl. 167, 114195. https://doi.org/10.1016/j.eswa.2020.114195 (2021).

Article Google Scholar

Milinda, H. & Madhusanka, B. Mud and dirt separation method for floor cleaning robot. In 2017 Moratuwa Engineering Research Conference (MERCon), 316320 (IEEE, 2017).

Canedo, D., Fonseca, P., Georgieva, P. & Neves, A. J. A deep learning-based dirt detection computer vision system for floor-cleaning robots with improved data collection. Technologies 9, 94 (2021).

Article Google Scholar

Canedo, D., Fonseca, P., Georgieva, P. & Neves, A.J. An innovative vision system for floor-cleaning robots based on yolov5. In Iberian Conference on Pattern Recognition and Image Analysis, 378389 (Springer, 2022).

Bormann, R., Weisshardt, F., Arbeiter, G. & Fischer, J. Autonomous dirt detection for cleaning in office environments. In 2013 IEEE International Conference on Robotics and Automation, 12601267 (IEEE, 2013).

Zhou, F., Zhao, H. & Nie, Z. Safety helmet detection based on yolov5. In 2021 IEEE International Conference on Power Electronics, Computer Applications (ICPECA), 611 (2021).

Junior, L. C.M. & Alfredo C.Ulson, J. Real time weed detection using computer vision and deep learning. In 2021 14th IEEE International Conference on Industry Applications (INDUSCON), 11311137, 10.1109/INDUSCON51756.2021.9529761 (2021).

Xu, R., Lin, H., Lu, K., Cao, L. & Liu, Y. A forest fire detection system based on ensemble learning. Forestshttps://doi.org/10.3390/f12020217 (2021).

Article Google Scholar

Yao, J. et al. A real-time detection algorithm for kiwifruit defects based on yolov5. Electronicshttps://doi.org/10.3390/electronics10141711 (2021).

Article Google Scholar

Redmon, J., Divvala, S., Girshick, R. & Farhadi, A. You only look once: Unified, real-time object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016).

Redmon, J. & Farhadi, A. Yolov3: An incremental improvement. CoRR (2018). arxiv:1804.02767.

Goodfellow, I.J., Warde-Farley, D., Mirza, M., Courville, A. & Bengio, Y. Maxout networks. In Proceedings of the 30th International Conference on International Conference on Machine Learning Volume 28, ICML13, III-1319-III-1327 (JMLR.org, 2013).

Wang, C. etal. Cspnet: A new backbone that can enhance learning capability of CNN. CoRR (2019). arxiv:1911.11929.

Bewley, A., Ge, Z., Ott, L., Ramos, F. & Upcroft, B. Simple online and realtime tracking. In 2016 IEEE International Conference on Image Processing (ICIP), 34643468 (2016).

Kuhn, H. W. The Hungarian method for the assignment problem. Naval Res. Logist. (NRL) 52, 721 (2010).

Article MATH Google Scholar

Kalman, R. E. A new approach to linear filtering and prediction problems. J. Basic Eng. 82, 3545. https://doi.org/10.1115/1.3662552 (1960).

Article MathSciNet Google Scholar

Canedo, D., Fonseca, P., Georgieva, P. & Neves, A. J. R. A deep learning-based dirt detection computer vision system for floor-cleaning robots with improved data collection. Technologieshttps://doi.org/10.3390/technologies9040094 (2021).

Article Google Scholar

Yan, Z. et al. Robot perception of static and dynamic objects with an autonomous floor scrubber. Intell. Serv. Robot.https://doi.org/10.1007/s11370-020-00324-9 (2020).

Article Google Scholar

Xu, Y. & Goodacre, R. On splitting training and validation set: A comparative study of cross-validation, bootstrap and systematic sampling for estimating the generalization performance of supervised learning. J. Anal. Test. 2, 249262 (2018).

Article PubMed PubMed Central Google Scholar

Dobbin, K. K. & Simon, R. M. Optimally splitting cases for training and testing high dimensional classifiers. BMC Med. Genomics 4, 3131 (2010).

Article Google Scholar

Pan, S. J. & Yang, Q. A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22, 13451359. https://doi.org/10.1109/TKDE.2009.191 (2010).

Article Google Scholar

Bottou, L. Large-scale machine learning with stochastic gradient descent. In International Conference on Computational Statistics (2010).

Targ, S., Almeida, D. & Lyman, K. Resnet in resnet: Generalizing residual architectures. CoRR (2016). arxiv:1603.08029.

Chiu, Y.-C., Tsai, C.-Y., Ruan, M.-D., Shen, G.-Y. & Lee, T.-T. Mobilenet-ssdv2: An improved object detection model for embedded systems. In 2020 International Conference on System Science and Engineering (ICSSE), 15, 10.1109/ICSSE50014.2020.9219319 (2020).

Yang, X. et al. Remote sensing image detection based on yolov4 improvements. IEEE Access 10, 9552795538. https://doi.org/10.1109/ACCESS.2022.3204053 (2022).

Article Google Scholar

Muzammul, M. & Li, X. A survey on deep domain adaptation and tiny object detection challenges, techniques and datasets, arXiv:2107.07927 (2021).

Iyer, R., Bhensdadiya, K. & Ringe, P. Comparison of yolov3, yolov5s and mobilenet-ssd v2 for real-time mask detection. Artic. Int. J. Res. Eng. Technol. 8, 11561160 (2021).

Google Scholar

Tan, L., Huangfu, T., Wu, L. & Chen, W. Comparison of yolo v3, faster r-cnn, and ssd for real-time pill identification, 10.21203/rs.3.rs-668895/v1 (2021).

Ahmed, K. R. Smart pothole detection using deep learning based on dilated convolution. Sensorshttps://doi.org/10.3390/s21248406 (2021).

Article PubMed PubMed Central Google Scholar

Here is the original post:

Vision-based dirt distribution mapping using deep learning | Scientific Reports - Nature.com

Comments are closed.