Undeclared Constructions: A Government’s Support Deep Learning Solution for Automatic Change Detection

Lezaun, Pamela Ferrari; Olivieri, Gustavo

doi:10.23919/itu-wt.2018.8598122

Cited by 4 publications

(3 citation statements)

References 1 publication

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“…A method based on SpaceNet has been proposed to detect changes in cities and automatically display the latest construction. 49 However, this method is difficult to use because of time and space constraints on the satellite images. Ostankovich and Afanasyev 50 used GoogLeNet and Cadastral Map for the automatic detection of illegal construction; however, a high false detection rate occurred in this method.…”

Section: Methods Of Deep Learningmentioning

confidence: 99%

“…With the rapid developments in deep learning technologies, the P of illegal construction recognition has significantly improved. A method based on SpaceNet has been proposed to detect changes in cities and automatically display the latest construction 49 . However, this method is difficult to use because of time and space constraints on the satellite images.…”

Section: Related Workmentioning

confidence: 99%

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High-precision automatic detection method of illegal construction object images in complex scenes

et al. 2022

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Illegal construction should be detected as early as possible as it can damage the environment and economy. However, the existing methods for detecting illegal construction can be improved in terms of their detection cycles, accuracy, and speed. Moreover, there are relatively few valuable real-world image datasets for detecting illegal construction. To address these issues, a high-precision real-time detection model named YEMNet and a new large-scale dataset for detection of illegal construction objects (ICOS) are proposed herein. Our YEMNet is based on the You Only Look Once v3 object detection model; this model adopts a lightweight convolutional neural network called "EfficientNet" as the backbone for feature extraction. Then, YEMNet employs a new activation function Mish outside the backbone to achieve efficient optimization and strong generalization, thereby improving the recognition accuracy for ICOS in complicated scenes. Our proposed dataset comprises 15 categories and 13,701 photographs of ICOS captured under different conditions concerning weather, lighting, and natural scenes. Extensive experiments on the proposed dataset show that YEMNet achieves a mean average precision of 91.41% with fewer parameters, thereby outperforming state-of-the-art object detectors. Our dataset and code are available at https://github.com/king-king-king/ICOS-Dataset.

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Section: Methods Of Deep Learningmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

High-precision automatic detection method of illegal construction object images in complex scenes

et al. 2022

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“…This issue seriously threatens the economic and social stability of China [1] . In Hong Kong of China, illegal buildings often appear on rooftops where villages and towns live [2] . The prevalence of illegal buildings in Argentina is a serious concern that poses significant risks to urban planning, resource conservation, and public safety [3] .…”

Section: Introductionmentioning

confidence: 99%

A New High-Precision and Lightweight Detection Model for Illegal Construction Objects Based on Deep Learning

Liu,

Zhou,

Zhang

et al. 2024

Tsinghua Sci. Technol.

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Illegal construction has caused serious harm around the world. However, current methods are difficult to detect illegal construction activities in time, and the calculation complexity and the parameters of them are large. To solve these challenges, a new and unique detection method is proposed, which detects objects related to illegal buildings in time to discover illegal construction activities. Meanwhile, a new dataset and a high-precision and lightweight detector are proposed. The proposed detector is based on the algorithm You Only Look Once (YOLOv4). The use of DenseNet as the backbone of YDHNet enables better feature transfer and reuse, improves detection accuracy, and reduces computational costs. Meanwhile, depthwise separable convolution is employed to lightweight the neck and head to further reduce computational costs. Furthermore, H-swish is utilized to enhance non-linear feature extraction and improve detection accuracy. Experimental results illustrate that YDHNet realizes a mean average precision of 89.60% on the proposed dataset, which is 3.78% higher than YOLOv4. The computational cost and parameter count of YDHNet are 26.22 GFLOPs and 16.18 MB, respectively. Compared to YOLOv4 and other detectors, YDHNet not only has lower computational costs and higher detection accuracy, but also timely identifies illegal construction objects and automatically detects illegal construction activities.

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YMMNet: A More Accurate and Lightweight Detector of Illegal Buildings for Smart Cities

Liu,

Zhang,

Zhou

et al. 2024

IEEE Trans. Consumer Electron.

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Undeclared Constructions: A Government’s Support Deep Learning Solution for Automatic Change Detection

Cited by 4 publications

References 1 publication

High-precision automatic detection method of illegal construction object images in complex scenes

High-precision automatic detection method of illegal construction object images in complex scenes

A New High-Precision and Lightweight Detection Model for Illegal Construction Objects Based on Deep Learning

YMMNet: A More Accurate and Lightweight Detector of Illegal Buildings for Smart Cities

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