{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/waste-detection-in-pomerania-non-profit","title":"Waste detection in Pomerania: non-profit project for detecting waste in environment","arxiv_id":"2105.06808","date":"2021-05-12","proceeding":null,"authors":["Sylwia Majchrowska","Agnieszka Mikołajczyk","Maria Ferlin","Zuzanna Klawikowska","Marta A. Plantykow","Arkadiusz Kwasigroch","Karol Majek"],"abstract":"Waste pollution is one of the most significant environmental issues in the modern world. The importance of recycling is well known, either for economic or ecological reasons, and the industry demands high efficiency. Our team conducted comprehensive research on Artificial Intelligence usage in waste detection and classification to fight the world's waste pollution problem. As a result an open-source framework that enables the detection and classification of litter was developed. The final pipeline consists of two neural networks: one that detects litter and a second responsible for litter classification. Waste is classified into seven categories: bio, glass, metal and plastic, non-recyclable, other, paper and unknown. Our approach achieves up to 70% of average precision in waste detection and around 75% of classification accuracy on the test dataset. The code used in the studies is publicly available online.","url_abs":"https://arxiv.org/abs/2105.06808v1","url_pdf":"https://arxiv.org/pdf/2105.06808v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"waste-detection-in-pomerania-non-profit","repo_url":"https://github.com/wimlds-trojmiasto/detect-waste","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"instance-segmentation","task_name":"Instance Segmentation"},{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"self-supervised-learning","task_name":"Self-Supervised Learning"}],"methods":[{"method_slug":"1x1-convolution","method_name":"1x1 Convolution"},{"method_slug":"absolute-position-encodings","method_name":"Absolute Position Encodings"},{"method_slug":"adam","method_name":"Adam"},{"method_slug":"attention","method_name":"Attention"},{"method_slug":"average-pooling","method_name":"Average Pooling"},{"method_slug":"bpe","method_name":"BPE"},{"method_slug":"batch-normalization","method_name":"Batch Normalization"},{"method_slug":"bifpn","method_name":"BiFPN"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"depthwise-convolution","method_name":"Depthwise Convolution"},{"method_slug":"depthwise-separable-convolution","method_name":"Depthwise Separable Convolution"},{"method_slug":"detr","method_name":"Detr"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"efficientdet","method_name":"EfficientDet"},{"method_slug":"efficientnet","method_name":"EfficientNet"},{"method_slug":"feedforward-network","method_name":"Feedforward Network"},{"method_slug":"inverted-residual-block","method_name":"Inverted Residual Block"},{"method_slug":"label-smoothing","method_name":"Label Smoothing"},{"method_slug":"layer-normalization","method_name":"Layer Normalization"},{"method_slug":"linear-layer","method_name":"Linear Layer"},{"method_slug":"multi-head-attention","method_name":"Multi-Head Attention"},{"method_slug":"pointwise-convolution","method_name":"Pointwise Convolution"},{"method_slug":"position-wise-feed-forward-layer","method_name":"Position-Wise Feed-Forward Layer"},{"method_slug":"rmsprop","method_name":"RMSProp"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"sigmoid-activation","method_name":"Sigmoid Activation"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"squeeze-and-excitation-block","method_name":"Squeeze-and-Excitation Block"},{"method_slug":"transformer","method_name":"Transformer"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/object-detection-on-drinking-waste","task":"Object Detection","dataset":"Drinking Waste Classification","model":"EfficientDet-D2","rank_in_archive_order":1,"of":1,"metrics":{"AP50":"99.4"},"uses_additional_data":false},{"leaderboard":"/sota/object-detection-on-extended-taco-1","task":"Object Detection","dataset":"Extended TACO-1","model":"EfficientDet-D2","rank_in_archive_order":1,"of":1,"metrics":{"AP50":"56.8"},"uses_additional_data":false},{"leaderboard":"/sota/object-detection-on-extended-taco-7","task":"Object Detection","dataset":"Extended TACO-7","model":"EfficientDet-D2","rank_in_archive_order":1,"of":1,"metrics":{"mAP50":"16.2"},"uses_additional_data":false},{"leaderboard":"/sota/object-detection-on-mju-waste","task":"Object Detection","dataset":"MJU-Waste","model":"EfficientDet-D2","rank_in_archive_order":1,"of":1,"metrics":{"AP50":"97.9"},"uses_additional_data":false},{"leaderboard":"/sota/object-detection-on-uavvaste","task":"Object Detection","dataset":"UAVVaste","model":"EfficientDet-D2","rank_in_archive_order":1,"of":1,"metrics":{"AP50":"74.1"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}