Real-Time Construction Waste Reduction Using Unmanned Aerial Vehicle

Enegbuma, Wallace Imoudu; Bamgbade, Jibril Adewale; Ming, Cosmas Pang Han; Ohueri, Chukwuka Christian; Tanko, Bruno Lot; Ojoko, Edo Oga; Dodo, Yakubu Aminu; Kori, Sa'id

doi:10.4018/978-1-7998-0369-0.ch025

Cited by 2 publications

(1 citation statement)

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“…In addition, satellite imagery as a data source is prone to cloud cover, and the limitations of revisit periods restrict the application of related methods [13]. This paper utilizes unmanned aerial vehicle (UAV) to gather data, which have the characteristics of high resolution, wide monitoring range, and real-time capabilities [14,15]. Moreover, UAV can perform monitoring in hard-to-reach areas, providing a solid data foundation for extracting characteristic information of solid waste and identifying targets in various garbage scenarios [16,17].…”

Section: Introductionmentioning

confidence: 99%

A Practical Deep Learning Architecture for Large-Area Solid Wastes Monitoring Based on UAV Imagery

Liu,

Zhao,

Zhang

et al. 2024

Applied Sciences

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The development of global urbanization has brought about a significant amount of solid waste. These untreated wastes may be dumped in any corner, causing serious pollution to the environment. Thus, it is necessary to accurately obtain their distribution locations and detailed edge information. In this study, a practical deep learning network for recognizing solid waste piles over extensive areas using unmanned aerial vehicle (UAV) imagery has been proposed and verified. Firstly, a high-resolution dataset serving to solid waste detection was created based on UAV aerial data. Then, a dual-branch solid waste semantic segmentation model was constructed to address the characteristics of the integration of solid waste distribution with the environment and the irregular edge morphology. The Context feature branch is responsible for extracting high-level semantic features, while the Spatial feature branch is designed to capture fine-grained spatial details. After information fusion, the model obtained more comprehensive feature representation and segmentation ability. The effectiveness of the improvement was verified through ablation experiments and compared with 13 commonly used semantic segmentation models, demonstrating the advantages of the method in solid waste segmentation tasks, with an overall accuracy of over 94%, and a recall rate of 88.6%—much better than the best performing baselines. Finally, a spatial distribution map of solid waste over Jiaxing district, China was generated by the model inference, which assisted the environmental protection department in completing environmental management. The proposed method provides a feasible approach for the accurately monitoring of solid waste, so as to provide policy support for environmental protection.

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