MyJournals Home  

RSS FeedsRemote Sensing, Vol. 13, Pages 4213: Identifying Damaged Buildings in Aerial Images Using the Object Detection Method (Remote Sensing)

 
 

21 october 2021 06:27:07

 
Remote Sensing, Vol. 13, Pages 4213: Identifying Damaged Buildings in Aerial Images Using the Object Detection Method (Remote Sensing)
 


The collapse of buildings caused by the earthquake seriously threatened human lives and safety. So, the quick detection of collapsed buildings from post-earthquake images is essential for disaster relief and disaster damage assessment. Compared with the traditional building extraction methods, the methods based on convolutional neural networks perform better because it can automatically extract high-dimensional abstract features from images. However, there are still many problems with deep learning in the extraction of collapsed buildings. For example, due to the complex scenes after the earthquake, the collapsed buildings are easily confused with the background, so it is difficult to fully use the multiple features extracted by collapsed buildings, which leads to time consumption and low accuracy of collapsed buildings extraction when training the model. In addition, model training is prone to overfitting, which reduces the performance of model migration. This paper proposes to use the improved classic version of the you only look once model (YOLOv4) to detect collapsed buildings from the post-earthquake aerial images. Specifically, the k-means algorithm is used to optimally select the number and size of anchors from the image. We replace the Resblock in CSPDarkNet53 in YOLOv4 with the ResNext block to improve the backbone`s ability and the performance of classification. Furthermore, to replace the loss function of YOLOv4 with the Focal-EOIU loss function. The result shows that compared with the original YOLOv4 model, our proposed method can extract collapsed buildings more accurately. The AP (average precision) increased from 88.23% to 93.76%. The detection speed reached 32.7 f/s. Our method not only improves the accuracy but also enhances the detection speed of the collapsed buildings. Moreover, providing a basis for the detection of large-scale collapsed buildings in the future.


 
142 viewsCategory: Geology, Physics
 
Remote Sensing, Vol. 13, Pages 4212: Multi-Resolution STAP for Enhanced Ultra-Low-Altitude Target Detection (Remote Sensing)
Remote Sensing, Vol. 13, Pages 4214: Assessment of Shoreline Transformation Rates and Landslide Monitoring on the Bank of Kuibyshev Reservoir (Russia) Using Multi-Source Data (Remote Sensing)
 
 
blog comments powered by Disqus


MyJournals.org
The latest issues of all your favorite science journals on one page

Username:
Password:

Register | Retrieve

Search:

Physics


Copyright © 2008 - 2024 Indigonet Services B.V.. Contact: Tim Hulsen. Read here our privacy notice.
Other websites of Indigonet Services B.V.: Nieuws Vacatures News Tweets Nachrichten