Building Damage Detection from Post-Event Aerial Imagery Using Single Shot Multibox Detector

Li, Yundong; Hu, Wei; Han, Dong; Zhang, Xueyan

doi:10.3390/app9061128

Cited by 63 publications

(38 citation statements)

References 21 publications

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“…The extracted features are global, local features are ignored, and local features are more important for wood texture recognition. Convolutional neural networks have the characteristics of local connection and weight sharing [35][36][37][38][39][40], which can accelerate the training of the network and facilitate the extraction of local features. The deep convolutional autoencoder designed in this paper is shown in Figure 2.…”

Section: Methods Of the Local Feature Descriptor Based On The Convolumentioning

confidence: 99%

Recognition and Grasping of Disorderly Stacked Wood Planks Using a Local Image Patch and Point Pair Feature Method

Liu

Ding

et al. 2020

Sensors

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Considering the difficult problem of robot recognition and grasping in the scenario of disorderly stacked wooden planks, a recognition and positioning method based on local image features and point pair geometric features is proposed here and we define a local patch point pair feature. First, we used self-developed scanning equipment to collect images of wood boards and a robot to drive a RGB-D camera to collect images of disorderly stacked wooden planks. The image patches cut from these images were input to a convolutional autoencoder to train and obtain a local texture feature descriptor that is robust to changes in perspective. Then, the small image patches around the point pairs of the plank model are extracted, and input into the trained encoder to obtain the feature vector of the image patch, combining the point pair geometric feature information to form a feature description code expressing the characteristics of the plank. After that, the robot drives the RGB-D camera to collect the local image patches of the point pairs in the area to be grasped in the scene of the stacked wooden planks, also obtaining the feature description code of the wooden planks to be grasped. Finally, through the process of point pair feature matching, pose voting and clustering, the pose of the plank to be grasped is determined. The robot grasping experiment here shows that both the recognition rate and grasping success rate of planks are high, reaching 95.3% and 93.8%, respectively. Compared with the traditional point pair feature method (PPF) and other methods, the method present here has obvious advantages and can be applied to stacked wood plank grasping environments.

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Section: Methods Of the Local Feature Descriptor Based On The Convolumentioning

confidence: 99%

Recognition and Grasping of Disorderly Stacked Wood Planks Using a Local Image Patch and Point Pair Feature Method

Liu

Ding

et al. 2020

Sensors

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“…A different approach was taken by Li et al [62], who used a convolutional autoencoder (CAE) that was trained using unlabelled post-disaster imagery based on SLIC superpixels, with results being finetuned by a CNN classifier. In follow-up work [63] the authors in addition employed a range of data augmentation methods, such as data blurring or rotating, to enlarge the number of samples. The resulting pre-training improved the overall damage detection accuracy by 10%.…”

Section: Advanced Machine Learning and The Emergence Of Cnnmentioning

confidence: 99%

“…Creative choice of class names is further hindering a comparison between different studies. Li et al [63] used the classes mildly damaged and ruins, while Xu et al [54] mapped categories including roof, ground, debris, and small objects. The difficulty of image-based damage mapping has led to a focus on severe damage classes (D4-5), making studies such as [42] that expressly focus on lesser damage (D2-3) an exception.…”

Section: Levels Of Disaster Damage Mappingmentioning

confidence: 99%

UAV-Based Structural Damage Mapping: A Review

Kerle

Nex

Gerke

et al. 2019

IJGI

134

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Structural disaster damage detection and characterization is one of the oldest remote sensing challenges, and the utility of virtually every type of active and passive sensor deployed on various air- and spaceborne platforms has been assessed. The proliferation and growing sophistication of unmanned aerial vehicles (UAVs) in recent years has opened up many new opportunities for damage mapping, due to the high spatial resolution, the resulting stereo images and derivatives, and the flexibility of the platform. This study provides a comprehensive review of how UAV-based damage mapping has evolved from providing simple descriptive overviews of a disaster science, to more sophisticated texture and segmentation-based approaches, and finally to studies using advanced deep learning approaches, as well as multi-temporal and multi-perspective imagery to provide comprehensive damage descriptions. The paper further reviews studies on the utility of the developed mapping strategies and image processing pipelines for first responders, focusing especially on outcomes of two recent European research projects, RECONASS (Reconstruction and Recovery Planning: Rapid and Continuously Updated Construction Damage, and Related Needs Assessment) and INACHUS (Technological and Methodological Solutions for Integrated Wide Area Situation Awareness and Survivor Localization to Support Search and Rescue Teams). Finally, recent and emerging developments are reviewed, such as recent improvements in machine learning, increasing mapping autonomy, damage mapping in interior, GPS-denied environments, the utility of UAVs for infrastructure mapping and maintenance, as well as the emergence of UAVs with robotic abilities.

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“…The collapsed buildings were successfully identified by analyzing the difference between pre-and post-event DHMs. The single-short multibit detector (SSD) [11] based on CNN pre-trained on the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) classification-localization (CLS-LOC) dataset was applied to detect building damage using extremely few training samples [12]. The experiment proved that the pre-training method can effectively increase various indicators of the model.…”

Section: Introductionmentioning

confidence: 99%

Discrimination of Earthquake-Induced Building Destruction from Space Using a Pretrained CNN Model

Liu

Zhang

et al. 2020

Applied Sciences

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The building is an indispensable part of human life which provides a place for people to live, study, work, and engage in various cultural and social activities. People are exposed to earthquakes, and damaged buildings caused by earthquakes are one of the main threats. It is essential to retrieve the detailed information of affected buildings after earthquakes. Very high-resolution satellite imagery plays a key role in retrieving building damage information since it captures imagery quickly and effectively after the disaster. In this paper, the pretrained Visual Geometry Group (VGG)Net model was applied for identifying collapsed buildings induced by the 2010 Haiti earthquake using pre- and post-event remotely sensed space imagery, and the fine-tuned pretrained VGGNet model was compared with the VGGNet model trained from scratch. The effects of dataset augmentation and freezing different intermediate layers were also explored. The experimental results demonstrated that the fine-tuned VGGNet model outperformed the VGGNet model trained from scratch with increasing overall accuracy (OA) from 83.38% to 85.19% and Kappa from 60.69% to 67.14%. By taking advantage of dataset augmentation, OA and Kappa went up to 88.83% and 75.33% respectively, and the collapsed buildings were better recognized with a larger producer accuracy of 86.31%. The present study showed the potential of using the pretrained Convolutional Neural Network (CNN) model to identify collapsed buildings caused by earthquakes using very high-resolution satellite imagery.

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Building Damage Detection from Post-Event Aerial Imagery Using Single Shot Multibox Detector

Cited by 63 publications

References 21 publications

Recognition and Grasping of Disorderly Stacked Wood Planks Using a Local Image Patch and Point Pair Feature Method

Recognition and Grasping of Disorderly Stacked Wood Planks Using a Local Image Patch and Point Pair Feature Method

UAV-Based Structural Damage Mapping: A Review

Discrimination of Earthquake-Induced Building Destruction from Space Using a Pretrained CNN Model

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