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Automated litter detection can help assess waste occurrences in the environment. Different machine learning solutions have been explored to develop litter detection tools, thereby supporting research, citizen science, and volunteer clean-up initiatives. However, to the best of our knowledge, no work has investigated the performance of state-of-the-art deep learning object detection approaches in the context of litter detection. In particular, no studies have focused on the assessment of those methods aiming their use in devices with low processing capabilities, e.g., mobile phones, typically employed in citizen science activities. In this paper, we fill this literature gap. We performed a comparative study involving state-of-the-art CNN architectures (e.g., Faster RCNN, Mask-RCNN, EfficientDet, RetinaNet and YOLO-v5), two litter image datasets and a smartphone. We also introduce a new dataset for litter detection, named PlastOPol, composed of 2418 images and 5300 annotations. The experimental results demonstrate that object detectors based on the YOLO family are promising for the construction of litter detection solutions, with superior performance in terms of detection accuracy, processing time, and memory footprint.<\/jats:p>","DOI":"10.3390\/s22020548","type":"journal-article","created":{"date-parts":[[2022,1,11]],"date-time":"2022-01-11T20:33:04Z","timestamp":1641933184000},"page":"548","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":54,"title":["Litter Detection with Deep Learning: A Comparative Study"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6527-6740","authenticated-orcid":false,"given":"Manuel","family":"C\u00f3rdova","sequence":"first","affiliation":[{"name":"Institute of Computing, University of Campinas, Avenue Albert Einstein, Campinas 13083-852, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3765-8300","authenticated-orcid":false,"given":"Allan","family":"Pinto","sequence":"additional","affiliation":[{"name":"Brazilian Center for Research in Energy and Materials (CNPEM), Brazilian Synchrotron Light Laboratory (LNLS), Campinas 13083-100, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1588-1926","authenticated-orcid":false,"given":"Christina Carrozzo","family":"Hellevik","sequence":"additional","affiliation":[{"name":"Department of International Business, NTNU\u2014Norwegian University of Science and Technology, Larsg\u00e5rdsvegen 2, 6009 Alesund, Norway"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6378-0900","authenticated-orcid":false,"given":"Saleh Abdel-Afou","family":"Alaliyat","sequence":"additional","affiliation":[{"name":"Department of ICT and Natural Sciences, NTNU\u2014Norwegian University of Science and Technology, Larsg\u00e5rdsvegen 2, 6009 Alesund, Norway"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1252-260X","authenticated-orcid":false,"given":"Ibrahim A.","family":"Hameed","sequence":"additional","affiliation":[{"name":"Department of ICT and Natural Sciences, NTNU\u2014Norwegian University of Science and Technology, Larsg\u00e5rdsvegen 2, 6009 Alesund, Norway"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0125-630X","authenticated-orcid":false,"given":"Helio","family":"Pedrini","sequence":"additional","affiliation":[{"name":"Institute of Computing, University of Campinas, Avenue Albert Einstein, Campinas 13083-852, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9772-263X","authenticated-orcid":false,"given":"Ricardo da S.","family":"Torres","sequence":"additional","affiliation":[{"name":"Department of ICT and Natural Sciences, NTNU\u2014Norwegian University of Science and Technology, Larsg\u00e5rdsvegen 2, 6009 Alesund, Norway"},{"name":"Farm Technology Group and Wageningen Data Competence Center, Wageningen University and Research, 6708 PB Wageningen, The Netherlands"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"838","DOI":"10.1126\/science.1094559","article-title":"Lost at Sea: Where Is All the Plastic?","volume":"304","author":"Thompson","year":"2004","journal-title":"Science"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"114365","DOI":"10.1016\/j.envpol.2020.114365","article-title":"Investigating the distribution and regional occurrence of anthropogenic litter in English marine protected areas using 25 years of citizen-science beach clean data","volume":"263","author":"Nelms","year":"2020","journal-title":"Environ. Pollut."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"61955","DOI":"10.1109\/ACCESS.2021.3073903","article-title":"Pansharpening PRISMA Data for Marine Plastic Litter Detection Using Plastic Indexes","volume":"9","author":"Kremezi","year":"2021","journal-title":"IEEE Access"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Freitas, S., Silva, H., and da Silva, E.P. (2021). Remote Hyperspectral Imaging Acquisition and Characterization for Marine Litter Detection. Remote Sens., 13.","DOI":"10.3390\/rs13132536"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Balsi, M., Moroni, M., Chiarabini, V., and Tanda, G. (2021). High-Resolution Aerial Detection of Marine Plastic Litter by Hyperspectral Sensing. Remote Sens., 13.","DOI":"10.3390\/rs13081557"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1016\/j.marpolbul.2014.12.041","article-title":"The impact of debris on marine life","volume":"92","author":"Gall","year":"2015","journal-title":"Mar. Pollut. Bull."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"302","DOI":"10.1890\/14-2070.1","article-title":"The ecological impacts of marine debris: Unraveling the demonstrated evidence from what is perceived","volume":"97","author":"Rochman","year":"2016","journal-title":"Ecology"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"419","DOI":"10.1016\/j.jum.2021.05.002","article-title":"Pathways to sustainable waste management in Indian Smart Cities","volume":"10","author":"Cheela","year":"2021","journal-title":"J. Urban Manag."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Lazcano, R.F., Vincent, A.E.S., and Hoellein, T.J. (2020). Trash Dance: Anthropogenic Litter and Organic Matter Co-Accumulate on Urban Beaches. Geosciences, 10.","DOI":"10.3390\/geosciences10090335"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.marenvres.2016.07.004","article-title":"Dangerous hitchhikers? Evidence for potentially pathogenic Vibrio spp. on microplastic particles","volume":"120","author":"Kirstein","year":"2016","journal-title":"Mar. Environ. Res."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Gayathri, N., Divagaran, A.R., Akhilesh, C.D., Aswiin, V.M., and Charan, N. (2021, January 19\u201320). IOT Based Smart Waste Management System. Proceedings of the 2021 7th International Conference on Advanced Computing and Communication Systems (ICACCS), Coimbatore, India.","DOI":"10.1109\/ICACCS51430.2021.9441819"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Sivasangari, A., Polishetty, U.R., and Gomathi, R.M. (, January 8\u201310). IoT based Smart Garbage System. Proceedings of the 2021 5th International Conference on Computing Methodologies and Communication (ICCMC), Erode, India.","DOI":"10.1109\/ICCMC51019.2021.9418455"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Badve, M., Chaudhari, A., Davda, P., Bagaria, V., and Kalbande, D. (2020, January 7\u20139). Garbage Collection System using IoT for Smart City. Proceedings of the 2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), Palladam, India.","DOI":"10.1109\/I-SMAC49090.2020.9243387"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"104950","DOI":"10.1016\/j.ocecoaman.2019.104950","article-title":"Automating the characterisation of beach microplastics through the application of image analyses","volume":"182","author":"Gauci","year":"2019","journal-title":"Ocean Coast. Manag."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.marpolbul.2017.11.045","article-title":"Mapping coastal marine debris using aerial imagery and spatial analysis","volume":"132","author":"Moy","year":"2018","journal-title":"Mar. Pollut. Bull."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"116730","DOI":"10.1016\/j.envpol.2021.116730","article-title":"Enabling a large-scale assessment of litter along Saudi Arabian red sea shores by combining drones and machine learning","volume":"277","author":"Martin","year":"2021","journal-title":"Environ. Pollut."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1016\/j.chemosphere.2018.10.084","article-title":"Use of a convolutional neural network for the classification of microbeads in urban wastewater","volume":"216","author":"Yurtsever","year":"2019","journal-title":"Chemosphere"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"116490","DOI":"10.1016\/j.envpol.2021.116490","article-title":"Automatic detection and quantification of floating marine macro-litter in aerial images: Introducing a novel deep learning approach connected to a web application in R","volume":"273","author":"Borrell","year":"2021","journal-title":"Environ. Pollut."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"111974","DOI":"10.1016\/j.marpolbul.2021.111974","article-title":"Automatic detection of seafloor marine litter using towed camera images and deep learning","volume":"164","author":"Politikos","year":"2021","journal-title":"Mar. Pollut. Bull."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1007\/s11263-019-01247-4","article-title":"Deep Learning for Generic Object Detection: A Survey","volume":"128","author":"Liu","year":"2020","journal-title":"Int. J. Comput. Vis."},{"key":"ref_21","unstructured":"Bengio, Y., and LeCun, Y. (2015, January 7\u20139). Very Deep Convolutional Networks for Large-Scale Image Recognition. Proceedings of the 3rd International Conference on Learning Representations, ICLR, San Diego, CA, USA. Conference Track Proceedings."},{"key":"ref_22","unstructured":"Howard, A., Pang, R., Adam, H., Le, Q.V., Sandler, M., Chen, B., Wang, W., Chen, L., Tan, M., and Chu, G. (November, January 27). Searching for MobileNetV3. Proceedings of the IEEE\/CVF International Conference on Computer Vision, ICCV, Seoul, Korea."},{"key":"ref_23","unstructured":"Wang, R.J., Li, X., Ao, S., and Ling, C.X. (May, January 30). Pelee: A Real-Time Object Detection System on Mobile Devices. Proceedings of the 6th International Conference on Learning Representations, (ICLR), Vancouver, BC, Canada. Available online: OpenReview.net."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"21242","DOI":"10.1109\/ACCESS.2018.2824240","article-title":"Data-Fusion Techniques for Open-Set Recognition Problems","volume":"6","author":"Neira","year":"2018","journal-title":"IEEE Access"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"447","DOI":"10.1109\/TWC.2019.2946140","article-title":"Edge AI: On-Demand Accelerating Deep Neural Network Inference via Edge Computing","volume":"19","author":"Li","year":"2020","journal-title":"IEEE Trans. Wirel. Commun."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Choudhary, T., Mishra, V., Goswami, A., and Sarangapani, J. (2020). A comprehensive survey on model compression and acceleration. Artif. Intell. Rev.","DOI":"10.1007\/s10462-020-09816-7"},{"key":"ref_27","unstructured":"Han, S., Mao, H., and Dally, W.J. (2016, January 2\u20134). Deep Compression: Compressing Deep Neural Network with Pruning, Trained Quantization and Huffman Coding. Proceedings of the 4th International Conference on Learning Representations (ICLR), San Juan, Puerto Rico."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","article-title":"Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks","volume":"39","author":"Ren","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"386","DOI":"10.1109\/TPAMI.2018.2844175","article-title":"Mask R-CNN","volume":"42","author":"He","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Tan, M., Pang, R., and Le, Q.V. (2020, January 13\u201319). EfficientDet: Scalable and Efficient Object Detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01079"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1109\/TPAMI.2018.2858826","article-title":"Focal Loss for Dense Object Detection","volume":"42","author":"Lin","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_32","unstructured":"Jocher, G., Stoken, A., Borovec, J., Hogan, A., Diaconu, L., Ingham, F., and Poznanski, J. (2021, July 06). ultralytics\/yolov5: Initial Release. Available online: https:\/\/zenodo.org\/record\/3908560#.Ydw0RLtOmV4."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Wang, T., Cai, Y., Liang, L., and Ye, D. (2020). A Multi-Level Approach to Waste Object Segmentation. Sensors, 20.","DOI":"10.3390\/s20143816"},{"key":"ref_34","unstructured":"Yang, M., and Thung, G. (2021, July 06). Classification of trash for recyclability status. CS229 Proj. Rep., Available online: http:\/\/cs229.stanford.edu\/proj2016\/report\/ThungYang-ClassificationOfTrashForRecyclabilityStatus-report.pdf."},{"key":"ref_35","unstructured":"Bashkirova, D., Zhu, Z., Akl, J., Alladkani, F., Hu, P., Ablavsky, V., Calli, B., Adel Bargal, S., and Saenko, K. (2021). ZeroWaste dataset: Towards Automated Waste Recycling. arXiv."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1109\/MRA.2021.3066040","article-title":"Robotic Waste Sorting Technology: Toward a Vision-Based Categorization System for the Industrial Robotic Separation of Recyclable Waste","volume":"28","author":"Koskinopoulou","year":"2021","journal-title":"IEEE Robot. Autom. Mag."},{"key":"ref_37","unstructured":"Proen\u00e7a, P.F., and Sim\u00f5es, P. (2020). TACO: Trash Annotations in Context for Litter Detection. arXiv."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Johari, A., and Swami, P.D. (2020, January 28\u201329). Comparison of Autonomy and Study of Deep Learning Tools for Object Detection in Autonomous Self Driving Vehicles. Proceedings of the 2nd International Conference on Data, Engineering and Applications (IDEA), Bhopal, India.","DOI":"10.1109\/IDEA49133.2020.9170659"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1109\/TETCI.2020.3034606","article-title":"Attention Deep Model With Multi-Scale Deep Supervision for Person Re-Identification","volume":"5","author":"Wu","year":"2021","journal-title":"IEEE Trans. Emerg. Top. Comput. Intell."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Abagiu, M., Popescu, D., Manta, F.L., and Popescu, L.C. (2020, January 22\u201323). Use of a Deep Neural Network for Object Detection in a Mobile Robot Application. Proceedings of the 2020 International Conference and Exposition on Electrical And Power Engineering (EPE), Iasi, Romania.","DOI":"10.1109\/EPE50722.2020.9305648"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Goyal, P., Girshick, R., He, K., and Dollar, P. (2017, January 22\u201329). Focal Loss for Dense Object Detection. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Girshick, R. (2015, January 7\u201313). Fast R-CNN. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Santiago, Chile.","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Dollar, P., and Girshick, R. (2017, January 22\u201329). Mask R-CNN. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.322"},{"key":"ref_44","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 (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_45","unstructured":"Chaudhuri, K., and Salakhutdinov, R. (2019, January 9\u201315). EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. Proceedings of the PMLR\u201436th International Conference on Machine Learning, Long Beach, CA, USA. Machine Learning Research."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014, January 23\u201327). Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Wang, C., Liao, H.M., Wu, Y., Chen, P., Hsieh, J., and Yeh, I. (2020, January 14\u201319). CSPNet: A New Backbone that can Enhance Learning Capability of CNN. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, Seattle, WA, USA.","DOI":"10.1109\/CVPRW50498.2020.00203"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep Residual Learning for Image Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"100026","DOI":"10.1016\/j.cscee.2020.100026","article-title":"AquaVision: Automating the detection of waste in water bodies using deep transfer learning","volume":"2","author":"Panwar","year":"2020","journal-title":"Case Stud. Chem. Environ. Eng."},{"key":"ref_50","unstructured":"Bishop, C. (2006). Pattern Recognition and Machine Learning, Springer."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"81257","DOI":"10.1109\/ACCESS.2020.2987869","article-title":"On the Fusion of Text Detection Results: A Genetic Programming Approach","volume":"8","author":"Campana","year":"2020","journal-title":"IEEE Access"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A.G., Zhu, M., Zhmoginov, A., and Chen, L. (2018, January 18\u201323). MobileNetV2: Inverted Residuals and Linear Bottlenecks. Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00474"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Zhang, X., Zhou, X., Lin, M., and Sun, J. (2018, January 18\u201323). ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00716"},{"key":"ref_54","unstructured":"Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., and Garnett, R. (2018). Pelee: A Real-Time Object Detection System on Mobile Devices. Advances in Neural Information Processing Systems 31, Curran Associates, Inc."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/2\/548\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T14:35:15Z","timestamp":1760366115000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/2\/548"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,11]]},"references-count":54,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2022,1]]}},"alternative-id":["s22020548"],"URL":"https:\/\/doi.org\/10.3390\/s22020548","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1,11]]}}}