{"title":"基于计算机视觉的建筑和拆除废弃物识别算法","authors":"Tomáš Zbíral, Václav Nežerka","doi":"10.4028/p-mj94xc","DOIUrl":null,"url":null,"abstract":"The construction industry generates a significant amount of waste, posing challenges for efficient waste management and resource recovery. This paper presents a preliminary study on the use of lightweight computer vision (CV) algorithms for the automatic recognition of construction and demolition waste (CDW) materials. Utilizing image datasets acquired by drones, the study aims to develop strategies for distinguishing between individual CDW materials based on the mean intensity gradient, brightness, and relative representation of color channels. Results indicate that the proposed method can effectively recognize crucial CDW materials, paving the way for potential applications in industry and geodesy. Further research is needed to test additional materials and metrics to refine the method for practical implementation.","PeriodicalId":46357,"journal":{"name":"Advances in Science and Technology-Research Journal","volume":"2 1","pages":"0"},"PeriodicalIF":1.0000,"publicationDate":"2023-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Computer Vision-Based Algorithms for Recognition of Construction and Demolition Waste Materials\",\"authors\":\"Tomáš Zbíral, Václav Nežerka\",\"doi\":\"10.4028/p-mj94xc\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The construction industry generates a significant amount of waste, posing challenges for efficient waste management and resource recovery. This paper presents a preliminary study on the use of lightweight computer vision (CV) algorithms for the automatic recognition of construction and demolition waste (CDW) materials. Utilizing image datasets acquired by drones, the study aims to develop strategies for distinguishing between individual CDW materials based on the mean intensity gradient, brightness, and relative representation of color channels. Results indicate that the proposed method can effectively recognize crucial CDW materials, paving the way for potential applications in industry and geodesy. Further research is needed to test additional materials and metrics to refine the method for practical implementation.\",\"PeriodicalId\":46357,\"journal\":{\"name\":\"Advances in Science and Technology-Research Journal\",\"volume\":\"2 1\",\"pages\":\"0\"},\"PeriodicalIF\":1.0000,\"publicationDate\":\"2023-10-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Advances in Science and Technology-Research Journal\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.4028/p-mj94xc\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ENGINEERING, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advances in Science and Technology-Research Journal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4028/p-mj94xc","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, MULTIDISCIPLINARY","Score":null,"Total":0}
Computer Vision-Based Algorithms for Recognition of Construction and Demolition Waste Materials
The construction industry generates a significant amount of waste, posing challenges for efficient waste management and resource recovery. This paper presents a preliminary study on the use of lightweight computer vision (CV) algorithms for the automatic recognition of construction and demolition waste (CDW) materials. Utilizing image datasets acquired by drones, the study aims to develop strategies for distinguishing between individual CDW materials based on the mean intensity gradient, brightness, and relative representation of color channels. Results indicate that the proposed method can effectively recognize crucial CDW materials, paving the way for potential applications in industry and geodesy. Further research is needed to test additional materials and metrics to refine the method for practical implementation.