文献类型：期刊文献

英文题名：Vision-Based On-Site Construction Waste Localization Using Unmanned Aerial Vehicle

作者：Wang, Zeli[1];Yang, Xincong[2];Zheng, Xianghan[3];Li, Heng[4]

机构：[1]East China Univ Sci & Technol, Dept Management Sci & Engn, Shanghai 200030, Peoples R China;[2]Harbin Inst Technol, Sch Civil & Environm Engn, Shenzhen 518055, Peoples R China;[3]Fuzhou Univ, Coll Math & Comp Sci, Fuzhou 350108, Peoples R China;[4]Hong Kong Polytech Univ, Dept Bldg & Real Estate, Hong Kong 999077, Peoples R China

年份：2024

卷号：24

期号：9

外文期刊名：SENSORS

收录：;EI(收录号：20242016077237);WOS:【SCI-EXPANDED(收录号:WOS:001219828600001)】；

基金：No Statement Available

语种：英文

外文关键词：construction waste management; unmanned aerial vehicle; computer vision; long-distance target detection

摘要：In the context of construction and demolition waste exacerbating environmental pollution, the lack of recycling technology has hindered the green development of the industry. Previous studies have explored robot-based automated recycling methods, but their efficiency is limited by movement speed and detection range, so there is an urgent need to integrate drones into the recycling field to improve construction waste management efficiency. Preliminary investigations have shown that previous construction waste recognition techniques are ineffective when applied to UAVs and also lack a method to accurately convert waste locations in images to actual coordinates. Therefore, this study proposes a new method for autonomously labeling the location of construction waste using UAVs. Using images captured by UAVs, we compiled an image dataset and proposed a high-precision, long-range construction waste recognition algorithm. In addition, we proposed a method to convert the pixel positions of targets to actual positions. Finally, the study verified the effectiveness of the proposed method through experiments. Experimental results demonstrated that the approach proposed in this study enhanced the discernibility of computer vision algorithms towards small targets and high-frequency details within images. In a construction waste localization task using drones, involving high-resolution image recognition, the accuracy and recall were significantly improved by about 2% at speeds of up to 28 fps. The results of this study can guarantee the efficient application of drones to construction sites.