Scene Recognition for Construction Projects Based on the Combination Detection of Detailed Ground Objects
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Published:2023-02-16
Issue:4
Volume:13
Page:2578
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ISSN:2076-3417
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Container-title:Applied Sciences
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language:en
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Short-container-title:Applied Sciences
Author:
Pu Jian12ORCID, Wang Zhigang12, Liu Renyu3, Xu Wensheng12, Shen Shengyu12, Zhang Tong3ORCID, Liu Jigen12
Affiliation:
1. Changjiang River Scientific Research Institute of Changjiang Water Resources Commission, Wuhan 430010, China 2. Research Center on Mountain Torrent & Geologic Disaster Prevention of the Ministry of Water Resources, Wuhan 430010, China 3. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University, Wuhan 430079, China
Abstract
The automatic identification of construction projects, which can be considered as complex scenes, is a technical challenge for the supervision of soil and water conservation in urban areas. Construction projects in high-resolution remote sensing images have no unified semantic definition, thereby exhibiting significant differences in image features. This paper proposes an identification method for construction projects based on the detection of detailed ground objects, which construction projects comprise, including movable slab houses, buildings under construction, dust screens, and bare soil (rock). To create the training data set, we select highly informative detailed ground objects from high-resolution remote sensing images. Then, the Faster RCNN (region-based convolutional neural network) algorithm is used to detect construction projects and the highly informative detailed ground objects separately. The merging of detection boxes and the correction of detailed ground object combinations are used to jointly improve the confidence of construction project detection results. The empirical experiments show that the accuracy evaluation indicators of this method on a data set of Wuhan construction projects outperform other comparative methods, and its AP value and F1 score reached 0.773 and 0.417, respectively. The proposed method can achieve satisfactory identification results for construction projects with complex scenes, and can be applied to the comprehensive supervision of soil and water conservation in construction projects.
Funder
Dynamic Monitoring of soil erosion and production construction project Supervision project of Wuhan in 2019
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
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