{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,12]],"date-time":"2026-08-12T18:57:59Z","timestamp":1786561079934,"version":"3.56.0"},"reference-count":54,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2022,8,18]],"date-time":"2022-08-18T00:00:00Z","timestamp":1660780800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Waste management is one of the challenges facing countries globally, leading to the need for innovative ways to design and operationalize smart waste bins for effective waste collection and management. The inability of extant waste bins to facilitate sorting of solid waste at the point of collection and the attendant impact on waste management process is the motivation for this study. The South African University of Technology (SAUoT) is used as a case study because solid waste management is an aspect where SAUoT is exerting an impact by leveraging emerging technologies. In this article, a convolutional neural network (CNN) based model called You-Only-Look-Once (YOLO) is employed as the object detection algorithm to facilitate the classification of waste according to various categories at the point of waste collection. Additionally, a nature-inspired search method is used as learning rate for the CNN model. The custom YOLO model was developed for waste object detection, trained with different weights and backbones, namely darknet53.conv.74, darknet19_448.conv.23, Yolov4.conv.137 and Yolov4-tiny.conv.29, respectively, for Yolov3, Yolov3-tiny, Yolov4 and Yolov4-tiny models. Eight (8) classes of waste and a total of 3171 waste images are used. The performance of YOLO models is considered in terms of accuracy of prediction (Average Precision\u2014AP) and speed of prediction measured in milliseconds. A lower loss value out of a percentage shows a higher performance of prediction and a lower value on speed of prediction. The results of the experiment show that Yolov3 has better accuracy of prediction as compared with Yolov3-tiny, Yolov4 and Yolov4-tiny. Although the Yolov3-tiny is quick at predicting waste objects, the accuracy of its prediction is limited. The mean AP (%) for each trained version of YOLO models is Yolov3 (80%), Yolov4-tiny (74%), Yolov3-tiny (57%) and Yolov4 (41%). This result of mAP (%) indicates that the Yolov3 model produces the best performance results (80%). In this regard, it is useful to implement a model that ensures accurate prediction to develop a smart waste bin system at the institution. The experimental results show the combination of KSA learning rate parameter of 0.0007 and Yolov3 is identified as the accurate model for waste object detection and classification. The use of nature-inspired search methods, such as the Kestrel-based Search Algorithm (KSA), has shown future prospect in terms of learning rate parameter determination in waste object detection and classification. Consequently, it is imperative for an EdgeIoT-enabled system to be equipped with Yolov3 for waste object detection and classification, thereby facilitating effective waste collection.<\/jats:p>","DOI":"10.3390\/s22166176","type":"journal-article","created":{"date-parts":[[2022,8,18]],"date-time":"2022-08-18T23:28:41Z","timestamp":1660865321000},"page":"6176","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["Nature-Inspired Search Method and Custom Waste Object Detection and Classification Model for Smart Waste Bin"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5718-4494","authenticated-orcid":false,"given":"Israel Edem","family":"Agbehadji","sequence":"first","affiliation":[{"name":"Honorary Research Associate, Faculty of Accounting and Informatics, Durban University of Technology, P.O. Box 1334, Durban 4000, South Africa"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3129-5246","authenticated-orcid":false,"given":"Abdultaofeek","family":"Abayomi","sequence":"additional","affiliation":[{"name":"Department of Information and Communication Technology, Mangosuthu University of Technology, P.O. Box 12363, Durban 4026, South Africa"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3427-8460","authenticated-orcid":false,"given":"Khac-Hoai Nam","family":"Bui","sequence":"additional","affiliation":[{"name":"Supercomputing Application Center, Korea Institute of Science and Technology Information (KISTI), Daejeon 34141, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7970-9615","authenticated-orcid":false,"given":"Richard C.","family":"Millham","sequence":"additional","affiliation":[{"name":"ICT and Society Research Group, Department of Information Technology, Durban University of Technology, P.O. Box 1334, Durban 4000, South Africa"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Emmanuel","family":"Freeman","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Ghana Communication Technology University, Accra PMB 100, Ghana"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,18]]},"reference":[{"key":"ref_1","first-page":"194","article-title":"Smart Garbage Bin Systems\u2014A Comprehensive Survey","volume":"Volume 808","author":"Venkataramani","year":"2018","journal-title":"Smart Secure Systems\u2014IoT and Analytics Perspective, Proceedings of the ICIIT 2017, Singapore, 27\u201329 December 2017"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Maulana, F.R., Widyanto, T.A.S., Pratama, Y., and Mutijarsa, K. (2018, January 10\u201311). Design and development of smart trash bin prototype for municipal solid waste management. Proceedings of the 2018 International Conference on ICT for Smart Society (ICISS), Semarang, Indonesia.","DOI":"10.1109\/ICTSS.2018.8550013"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"294","DOI":"10.1177\/0269094219851491","article-title":"The opportunities and value-adding activities of buy-back centres in South Africa\u2019s recycling industry: A value chain analysis","volume":"34","author":"Viljoen","year":"2019","journal-title":"Local Econ."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Ziouzios, D., and Dasygenis, M. (2019, January 20\u201322). A Smart Bin Implementantion using LoRa. Proceedings of the 4th South-East Europe Design Automation, Computer Engineering, Computer Networks and Social Media Conference, Piraeus, Greece.","DOI":"10.1109\/SEEDA-CECNSM.2019.8908523"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Rohit, G.S., Chandra, M.B., Saha, S., and Das, D. (2018, January 6\u20138). Smart Dual Dustbin Model for Waste Management in Smart Cities. Proceedings of the 2018 3rd International Conference for Convergence in Technology (I2CT), Pune, India.","DOI":"10.1109\/I2CT.2018.8529600"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Gupta, P.K., Shree, V., Hiremath, L., and Rajendran, S. (2019). The Use of Modern Technology in Smart Waste Management and Recycling: Artificial Intelligence and Machine Learning. Springer Nature.","DOI":"10.1007\/978-3-030-12500-4_11"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"485","DOI":"10.1016\/j.energy.2017.07.162","article-title":"Municipal waste management systems for domestic use","volume":"139","author":"Jouhara","year":"2017","journal-title":"Energy"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Al-Masri, E., Diabate, I., Jain, R., Lam, M.H., and Reddy Nathala, S. (2018, January 10\u201313). Recycle.io: An IoT-Enabled Framework for Urban Waste Management. Proceedings of the 2018 IEEE International Conference on Big Data (Big Data), Seattle, WA, USA.","DOI":"10.1109\/BigData.2018.8622117"},{"key":"ref_9","unstructured":"Agbehadji, I.E., Millham, R.C., Jung, J.J., Bui, K.-H.N., Fong, S., Abdultaofeek, A., and Frimpong, S.O. (November, January 29). Bio-inspired energy efficient clustering approach for wireless sensor networks. Proceedings of the 7th International Conference on Wireless Networks and Mobile Communications (WINCOM\u201919), Fez, Morocco."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1177\/1550147720908772","article-title":"Intelligent energy optimization for advanced IoT analytics edge computing on wireless sensor networks","volume":"16","author":"Agbehadji","year":"2020","journal-title":"Int. J. Distrib. Sens. Netw."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"200","DOI":"10.1016\/j.adhoc.2018.12.009","article-title":"An IoT-based smart cities infrastructure architecture applied to a waste management scenario","volume":"87","author":"Marques","year":"2019","journal-title":"Ad Hoc Netw."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Shyam, G.K., Manvi, S.S., and Bharti, P. (2017, January 23\u201324). Smart waste management using internet-of-things (IoT). Proceedings of the 2017 2nd International Conference on Computing and Communications Technologies (ICCCT), Chennai, India.","DOI":"10.1109\/ICCCT2.2017.7972276"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Bharadwaj, A.S., Rego, R., and Chowdhury, A. (2016, January 16\u201318). IoT based solid waste management system: A conceptual approach with an architectural solution as a smart city application. Proceedings of the 2016 IEEE Annual India Conference (INDICON), Bangalore, India.","DOI":"10.1109\/INDICON.2016.7839147"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Folianto, F., Yeow, W.L., and Low, Y.S. (2015, January 7\u20139). Smartbin: Smart waste management system. Proceedings of the 2015 IEEE Tenth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP), Singapore.","DOI":"10.1109\/ISSNIP.2015.7106974"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"646953","DOI":"10.1155\/2014\/646953","article-title":"IoT-based smart garbage system for efficient food waste management","volume":"2014","author":"Hong","year":"2014","journal-title":"Sci. World J."},{"key":"ref_16","first-page":"7043674","article-title":"A Proposed IoT-Enabled Smart Waste Bin Management System and Efficient Route Selection","volume":"2019","author":"Zeb","year":"2019","journal-title":"J. Comput. Netw. Commun."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1030","DOI":"10.1051\/matecconf\/201714001030","article-title":"Smart Bin: Internet-of-Things Garbage Monitoring System","volume":"140","author":"Mustafa","year":"2017","journal-title":"MATEC Web Conf."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Sreejith, S., Ramya, R., and Roja, R. (2019, January 15\u201316). Smart Bin For Waste Management System. Proceedings of the 2019 5th International Conference on Advanced Computing & Communication Systems (ICACCS), Coimbatore, India.","DOI":"10.1109\/ICACCS.2019.8728531"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Wijaya, A.S., Zainuddin, Z., and Niswar, M. (2017, January 9\u201311). Design a smart waste bin for smart waste management. Proceedings of the 2017 5th International Conference on Instrumentation Control, and Automation (ICA), Yogyakarta, Indonesia.","DOI":"10.1109\/ICA.2017.8068414"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Vu, D.D., and Kaddoum, G. (2017, January 2\u20133). A waste city management system for smart cities applications. Proceedings of the 2017 Advances in Wireless and Optical Communications (RTUWO), Riga, Latvia.","DOI":"10.1109\/RTUWO.2017.8228538"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Joshi, J., Reddy, J., Reddy, P., Agarwal, A., Agarwal, R., Bagga, A., and Bhargava, A. (2016, January 11\u201312). Cloud Computing Based Smart Garbage Monitoring System. Proceedings of the 2016 3rd International Conference on Electronic Design (ICED), Phuket, Thailand.","DOI":"10.1109\/ICED.2016.7804609"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"e12521","DOI":"10.1111\/exsy.12521","article-title":"Distributed artificial bee colony approach for connected appliances in smart home energy management system","volume":"37","author":"Bui","year":"2020","journal-title":"Expert Syst."},{"key":"ref_23","first-page":"3352","article-title":"Implementation of smartbin using convolutional neural networks","volume":"5","author":"Hulyalkar","year":"2018","journal-title":"Int. Res. J. Eng. Technol. (IRJET)"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Agbehadji, I.E., Awuzie, B.O., Ngowi, A.B., and Millham, R.C. (2020). Review of Big Data Analytics, Artificial Intelligence and Nature-inspired Computing Models towards Accurate Detection of COVID-19 Pandemic Cases and Contact Tracing. Int. J. Environ. Res. Public Health, 17.","DOI":"10.3390\/ijerph17155330"},{"key":"ref_25","unstructured":"Theano Development Team (2015). Deep Learning Tutorial Release 0.1, LISA lab, University of Montreal."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Agbehadji, I.E., Millham, R., Fong, S., and Hong, H.-J. (2018, January 9\u201312). Kestrel-based Search Algorithm (KSA) for parameter tuning unto Long Short Term Memory (LSTM) Network for feature selection in classification of high-dimensional bioinformatics datasets. Proceedings of the Federated Conference on Computer Science and Information Systems, Poznan, Poland.","DOI":"10.15439\/2018F52"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40537-014-0007-7","article-title":"Deep learning applications and challenges in big data analytics","volume":"2","author":"Najafabadi","year":"2015","journal-title":"J. Big Data"},{"key":"ref_28","unstructured":"Sunny, M.S.H., Dipta, D.R., Hossain, S., Faruque, H.M.R., and Hossain, E. (2019, January 3\u20135). Design of a Convolutional Neural Network Based Smart Waste Disposal System. Proceedings of the 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT), Dhaka, Bangladesh."},{"key":"ref_29","first-page":"281","article-title":"Solid waste bin detection and classification using Dynamic Time Warping and MLP classifier","volume":"34","author":"Arebey","year":"2013","journal-title":"Waste Manag."},{"key":"ref_30","first-page":"5699","article-title":"Deep reinforcement learning enabled smart city recycling waste object classification","volume":"71","author":"Abdelmaboud","year":"2022","journal-title":"Comput. Mater. Contin."},{"key":"ref_31","unstructured":"Kulkarni, H.N., and Raman, N.K.S. (2018). Waste Object Detection and Classification, Stanford University. CS230: Deep Learning, Winter 2018."},{"key":"ref_32","first-page":"1297","article-title":"IOT based smart dustbin","volume":"9","author":"Maddileti","year":"2020","journal-title":"Int. J. Sci. Technol. Res."},{"key":"ref_33","unstructured":"Sinha, A., and Couderc, P. (2013, January 24\u201329). Smart Bin for Incompatible Waste Items. Proceedings of the ICAS 2013, The Ninth International Conference on Autonomic and Autonomous Systems, Lisbon, Portugal."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Awuzie, B., and Monyane, T.G. (2020). Conceptualizing Sustainability Governance Implementation for Infrastructure Delivery Systems in Developing Countries: Success Factors. Sustainability, 12.","DOI":"10.3390\/su12030961"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Valente, M., Silva, H., Caldeira, J.M.L.P., Soares, V.N.G.J., and Gaspar, P.D. (2019). Detection of Waste Containers Using Computer Vision. Appl. Syst. Innov., 2.","DOI":"10.3390\/asi2010011"},{"key":"ref_36","unstructured":"Bochkovskiy, A., Wang, C.-Y., and Liao, H.-Y.M. (2020). YOLOv4: Optimal Speed and Accuracy of Object Detection. arXiv."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Huang, R., Pedoeem, J., and Chen, C. (2018). YOLO-LITE: A Real-Time Object Detection Algorithm Optimized for Non-GPU Computers. arXiv.","DOI":"10.1109\/BigData.2018.8621865"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 27\u201330). You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Redmon, J., and Farhadi, A. (2017, January 21\u201326). YOLO9000: Better, Faster, Stronger. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.690"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Yu, J., Jiang, Y., Wang, Z., Cao, Z., and Huang, T. (2016, January 15\u201319). UnitBox: An advanced object detection network. Proceedings of the 24th ACM International Conference on Multimedia, Amsterdam, The Netherlands.","DOI":"10.1145\/2964284.2967274"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Rezatofighi, H., Tsoi, N., Gwak, J.Y., Sadeghian, A., Reid, I., and Savarese, S. (2019, January 15\u201320). Generalized intersection over union: A metric and a loss for bounding box regression. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00075"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Zheng, Z., Wang, P., Liu, W., Li, J., Ye, R., and Ren, D. (2020, January 7\u201312). Distance-IoU Loss: Faster and better learning for bounding box regression. Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), New York, NY, USA.","DOI":"10.1609\/aaai.v34i07.6999"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"3212","DOI":"10.1109\/TNNLS.2018.2876865","article-title":"Object detection with deep learning: A review","volume":"30","author":"Zhao","year":"2019","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_44","unstructured":"Yu, L.H., Ganiyat, O.O., and Kim, S.-H. (2019, January 20\u201323). Automatic Classifications and Recognition for Recycled Garbage by Utilizing Deep Learning Technology. Proceedings of the 2019 7th International Conference on Information Technology: IoT and Smart City, Shanghai, China."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Agbehadji, I.E., Millham, R., and Fong, S. (2019, January 23). Kestrel-based search algorithm for association rule mining and classification of frequently changed items. Proceedings of the 2016 8th International Conference on Computational Intelligence and Communication Networks (CICN), Dehadrun, India.","DOI":"10.1109\/CICN.2016.76"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"222","DOI":"10.1504\/IJBIC.2019.100151","article-title":"Integration of Kestrel-based search algorithm with artificial neural network for feature subset selection","volume":"13","author":"Agbehadji","year":"2019","journal-title":"Int. J. Bio-Inspired Comput."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"9457821","DOI":"10.1155\/2018\/9457821","article-title":"Bioinspired computational approach to missing value estimation","volume":"2018","author":"Agbehadji","year":"2018","journal-title":"Math. Probl. Eng."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Zhang, X., Zhou, X., Lin, M., and Sun, J. (2017, January 21\u201326). Shufflenet: An extremely efficient convolutional neural network for mobile devices. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2018.00716"},{"key":"ref_49","first-page":"1","article-title":"Classification and Segregation of Garbage for Recyclability Process","volume":"9","author":"Shah","year":"2020","journal-title":"Int. J. Sci. Res."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1915","DOI":"10.5194\/isprs-archives-XLII-3-1915-2018","article-title":"Rapid target detection in high resolution remote sensing images using yolo model","volume":"42","author":"Wu","year":"2018","journal-title":"ISPRS-Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Li, Y., Zhao, Z., Luo, Y., and Qiu, Z. (2020). Real-Time Pattern-Recognition of GPR Images with YOLO v3 Implemented by Tensorflow. Sensors, 20.","DOI":"10.3390\/s20226476"},{"key":"ref_52","unstructured":"Redmon, J., and Farhadi, A. (2018). YOLOv3: An Incremental Improvement. arXiv."},{"key":"ref_53","unstructured":"Alderliesten, K. (2020, September 07). YOLOv3-Real-Time Object Detection. Available online: https:\/\/medium.com\/analytics-vidhya\/yolov3-real-time-object-detection-54e69037b6d0."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Kumar, S., Yadav, D., Gupta, H., Verma, O.P., Ansari, I.A., and Ahn, C.W. (2020). A Novel YOLOv3 Algorithm-Based Deep Learning Approach forWaste Segregation: Towards Smart Waste Management. Electronics, 10.","DOI":"10.3390\/electronics10010014"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/16\/6176\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:11:30Z","timestamp":1760141490000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/16\/6176"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,18]]},"references-count":54,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2022,8]]}},"alternative-id":["s22166176"],"URL":"https:\/\/doi.org\/10.3390\/s22166176","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,8,18]]}}}