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Proper garbage waste processing, management, and recycling are crucial for both ecological and economic reasons. Computer vision techniques have shown advanced capabilities in various applications, including object detection and classification. In this study, we conducted an extensive review of the use of artificial intelligence for garbage processing and management. However, a major limitation in this field is the lack of datasets containing top\u2010view images of garbage. We introduce a new dataset named \u201cKACHARA,\u201d containing 4727 images categorized into seven classes: clothes, decomposable (organic waste), glass, metal, paper, plastic, and wood. Importantly, the dataset exhibits a moderate imbalance, mirroring the distribution of real\u2010world garbage, which is crucial for training accurate classification models. For classification, we utilize transfer learning with the well\u2010known deep learning model MobileNetV3\u2010Large, where the top layers are fine\u2010tuned to enhance performance. We achieved a classification accuracy of 94.37% and also evaluated performance using precision, recall, F1\u2010score, and confusion matrix. These results demonstrate the model\u2019s strong generalization in aerial\/top\u2010view garbage classification.<\/jats:p>","DOI":"10.1155\/acis\/9106130","type":"journal-article","created":{"date-parts":[[2025,7,2]],"date-time":"2025-07-02T07:19:37Z","timestamp":1751440777000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Deep Learning Enabled Garbage Classification and Detection by Visual Context for Aerial Images"],"prefix":"10.1155","volume":"2025","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7145-1146","authenticated-orcid":false,"given":"Agnivesh","family":"Pandey","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1195-497X","authenticated-orcid":false,"given":"Rohit","family":"Raja","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4274-4927","authenticated-orcid":false,"given":"Manoj","family":"Gupta","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3842-4485","authenticated-orcid":false,"given":"Farhan A.","family":"Alenizi","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0059-2603","authenticated-orcid":false,"given":"Pannee","family":"Suanpang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1618-6001","authenticated-orcid":false,"given":"Aziz","family":"Nanthaamornphong","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2025,7,2]]},"reference":[{"key":"e_1_2_12_1_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jenvman.2019.03.025"},{"key":"e_1_2_12_2_2","doi-asserted-by":"publisher","DOI":"10.3390\/rs12091515"},{"key":"e_1_2_12_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.scitotenv.2016.05.084"},{"key":"e_1_2_12_4_2","article-title":"WasteNet: Waste Classification at the Edge for Smart Bins","author":"White G.","year":"2020","journal-title":"arXiv preprint arXiv:200605873"},{"key":"e_1_2_12_5_2","doi-asserted-by":"publisher","DOI":"10.3390\/sym14050960"},{"key":"e_1_2_12_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/access.2020.3010496"},{"key":"e_1_2_12_7_2","doi-asserted-by":"publisher","DOI":"10.1109\/access.2019.2959033"},{"key":"e_1_2_12_8_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jclepro.2020.120814"},{"key":"e_1_2_12_9_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-19651-6_41"},{"key":"e_1_2_12_10_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-20518-8_30"},{"key":"e_1_2_12_11_2","unstructured":"GyawaliD. 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Rethinking the Inception Architecture for Computer Vision Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition May 2016 2818\u20132826 https:\/\/doi.org\/10.1109\/cvpr.2016.308 2-s2.0-84986296808.","DOI":"10.1109\/CVPR.2016.308"},{"key":"e_1_2_12_19_2","doi-asserted-by":"crossref","unstructured":"HeK. ZhangX. RenS. andSunJ. Deep Residual Learning for Image Recognition Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition August 2016 770\u2013778 https:\/\/doi.org\/10.1109\/cvpr.2016.90 2-s2.0-84986274465.","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_2_12_20_2","article-title":"Efficient Convolutional Neural Networks for Mobile Vision Applications","author":"Howard A. G. 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