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ZHAOBenyun authored Mar 13, 2024
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<h1 style="text-align: center; font-size: 36px; font-family: 'Sama Devanagari';"> CUBIT: A High-Resolution Infrastructure Defect Dataset <br /> Fully Evaluated with Autonomous Detection Framework
<h1 style="text-align: center; font-size: 32px; font-family: 'Sama Devanagari';"> CUBIT Dataset
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<h3 style="text-align: center; font-size: 25px; font-family: 'Sama Devanagari';"> Submitted to International Conference on Acoustics, Speech, & Signal Processing 2024 (ICASSP 2024)
<h3 style="text-align: center; font-size: 25px; font-family: 'Sama Devanagari';">
High-Resolution Infrastructure Defect Detection Dataset Sourced by Unmanned Systems and Validated with Deep Learning
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<h3 style="text-align: center; font-size: 20px; font-family: 'Sama Devanagari';">
Automation in Construction 2024
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<div style=" text-align: center; font-size: 17px;">
Benyun Zhao<sup>1</sup>, Xunkuai Zhou<sup>2</sup>, Guidong Yang<sup>1</sup>, Junjie Wen<sup>1</sup>, Jihan Zhang<sup>1</sup>, Xi Chen<sup>1</sup>, and <a href="http://www.mae.cuhk.edu.hk/~bmchen/">Ben M. Chen</a><sup>1</sup>, IEEE Fellow
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Benyun Zhao<sup>1</sup>, Xunkuai Zhou<sup>2,1</sup>, Guidong Yang<sup>1</sup>, Junjie Wen<sup>1</sup>, Jihan Zhang<sup>1</sup>, Jia Dou<sup>1</sup>, Li Guang<sup>1</sup>, <br> Xi Chen<sup>1</sup>, and <a href="http://www.mae.cuhk.edu.hk/~bmchen/">Ben M. Chen</a><sup>1</sup> IEEE Fellow
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<div style="text-align: center; font-size: 17px;" >
1.Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong <br /> 2.School of Electronics and Information Engineering,Tongji University
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1.Department of Mechanical and Automation Engineering, <br>The Chinese University of Hong Kong <br /> 2.School of Electronics and Information Engineering,Tongji University
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<a href="#" style="color: white; text-decoration: none;">Paper (coming soon);</a>
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<a href="#" style="color: white; text-decoration: none;">Paper (coming soon)</a>
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<a href="https://zhaobenyun.github.io/CUBIT/ICASSP_2024_Appendix.pdf" style="color: white; text-decoration: none;">Supplementary;</a>
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<a href="https://zhaobenyun.github.io/CUBIT/" style="color: white; text-decoration: none;">Project Page;</a>
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Dataset will be available at <a href="https://drive.google.com/drive/folders/1LWwEKQ8rSB97fCRD4sG7b6UcK40rSdA2?usp=drive_link" style="color: white; text-decoration: none;">https://drive.google.com/drive/folders/1LWwEKQ8rSB97fCRD4sG7b6UcK40rSdA2?usp=drive_link</a>
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Dataset can be available at: <br><a href="https://drive.google.com/drive/folders/1LWwEKQ8rSB97fCRD4sG7b6UcK40rSdA2?usp=drive_link" style="color: white; text-decoration: none;">https://drive.google.com/drive/folders/1LWwEKQ8rSB97fCRD4sG7b6UcK40rSdA2?usp=drive_link</a>
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<h2>Abstract</h2>
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Learning-based visual inspection, integrated with unmanned robotic system, offers a more effective, efficient, and safer alternative for infrastructure inspection tasks that are traditionally heavily reliant on human labor. However, the potential of learning-based inspection methods remains limited due to the lack of publicly available, high-quality datasets. This paper presents CUBIT, a high-resolution defect detection dataset comprising more than $5500$ images with resolutions up to $8000\times6000$ which covers a broader spectrum of practical situations, backgrounds, and defect categories than existing publicly available datasets. We conduct extensive experiments to benchmark the performance of state-of-the-art real-time detection methods on our proposed dataset, validating the effectiveness of it. Moreover, based on the benchmark results, we develop a module named GIPFPP to integrate multi-scale feature, enhancing the AP by 3\% while reducing the number of parameters by 10\% on baseline model. Additionally, a real-site UAV-based inspection has been conducted to verify the reliability of the dataset.
Learning-based visual inspection, integrated with unmanned robotic system, offers a more effective, efficient, and safer alternative for infrastructure inspection tasks that are traditionally heavily reliant on human labor. However, the potential of learning-based inspection methods remains limited due to the lack of publicly available, high-quality datasets. This paper presents CUBIT, a high-resolution defect detection dataset comprising more than $5500$ images with resolutions up to $8000\times6000$ which covers a broader spectrum of practical situations, backgrounds, and defect categories than existing publicly available datasets. We conduct extensive experiments to benchmark the performance of state-of-the-art real-time detection methods on our proposed dataset, validating the effectiveness of it. Moreover, based on the benchmark results, we develop a module named GIPFPP to integrate multi-scale feature, enhancing the AP by 3\% while reducing the number of parameters by 10\% on baseline model. Additionally, a real-site UAV-based inspection has been conducted to verify the reliability of the dataset. -->
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<h2>Sample images in CUBIT</h2>
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<img src="./sample.png" width=80% height=80%>
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<h2>Defect Detection Framework based on CUBIT</h2>
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The visualization of defect detection framework based on CUBIT dataset is illustrated below, which encompasses the entire process: data collection by autonomous unmanned system; the baseline network integrated with our GIPFPP module; the output of defect detection results.
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<img src="./frame.png" width=80% height=80%>
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<h2>Acknowledgement</h2>
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