Joint Training of Image Generator and Detector for Road Defect Detection
Mitsubishi Electric Research Laboratories (MERL)（三菱电机研究实验室）
Kuan-Chuan Peng
备注：
This paper is accepted to ICCV 2025 Workshop on Representation Learning with Very Limited Resources: When Data, Modalities, Labels, and Computing Resources are Scarce as an oral paper
Road defect detection is important for road authorities to reduce the vehicle damage caused by road defects. Considering the practical scenarios where the defect detectors are typically deployed on edge devices with limited memory and computational resource, we aim at performing road defect detection without using ensemble-based methods or test-time augmentation (TTA). To this end, we propose to Jointly Train the image Generator and Detector for road defect detection (dubbed as JTGD). We design the dual discriminators for the generative model to enforce both the synthesized defect patches and overall images to look plausible. The synthesized image quality is improved by our proposed CLIP-based Fréchet Inception Distance loss. The generative model in JTGD is trained jointly with the detector to encourage the generative model to synthesize harder examples for the detector. Since harder synthesized images of better quality caused by the aforesaid design are used in the data augmentation, JTGD outperforms the state-of-the-art method in the RDD2022 road defect detection benchmark across various countries under the condition of no ensemble and TTA. JTGD only uses less than 20% of the number of parameters compared with the competing baseline, which makes it more suitable for deployment on edge devices in practice.
Establishing Reality-Virtuality Interconnections in Urban Digital Twins for Superior Intelligent Road Inspection and Simulation
College of Electronics & Information Engineering, Shanghai Research Institute for Intelligent Autonomous Systems, the State Key Laboratory of Intelligent Autonomous Systems, and Frontiers Science Center for Intelligent Autonomous Systems, Tongji University, Shanghai 201804, China（电子与信息工程学院，上海智能自主系统研究院，智能自主系统国家重点实验室，智能自主系统前沿科学中心，同济大学，上海）
Curtin University（科廷大学）；Gwangju Institute of Science and Technology (GIST)（光州科学技术院）；RMIT University（皇家墨尔本理工大学）
In the past few years, the performance of road defect detection has been remarkably improved thanks to advancements on various studies on computer vision and deep learning. Although a large-scale and well-annotated datasets enhance the performance of detecting road pavement defects to some extent, it is still challengeable to derive a model which can perform reliably for various road conditions in practice, because it is intractable to construct a dataset considering diverse road conditions and defect patterns. To end this, we propose an unsupervised approach to detecting road defects, using A…
The University of Queensland（昆士兰大学）；Logan City Council（洛根市议会）
With the rapid development of intelligent detection algorithms based on deep learning, much progress has been made in automatic road defect recognition and road marking parsing. This can effectively address the issue of an expensive and time-consuming process for professional inspectors to review the street manually. Towards this goal, we present RoadAtlas, a novel end-to-end integrated system that can support 1) road defect detection, 2) road marking parsing, 3) a web-based dashboard for presenting and inputting data by users, and 4) a backend containing a well-structured database and develop…
Centre for Visual Information Technology（视觉信息技术中心）；Kohli Center for Intelligent System（科利智能系统中心）；IIIT Hyderabad（海得拉巴国际信息技术学院）
Road networks in cities are massive and is a critical component of mobility. Fast response to defects, that can occur not only due to regular wear and tear but also because of extreme events like storms, is essential. Hence there is a need for an automated system that is quick, scalable and cost-effective for gathering information about defects. We propose a system for city-scale road audit, using some of the most recent developments in deep learning and semantic segmentation. For building and benchmarking the system, we curated a dataset which has annotations required for road defects. Howeve…
Most state-of-the-art approaches to road extraction from aerial images rely on a CNN trained to label road pixels as foreground and remainder of the image as background. The CNN is usually trained by minimizing pixel-wise losses, which is less than ideal to produce binary masks that preserve the road network's global connectivity. To address this issue, we introduce an Adversarial Learning (AL) strategy tailored for our purposes. A naive one would treat the segmentation network as a generator and would feed its output along with ground-truth segmentations to a discriminator. It would then trai…
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School of Artificial Intelligence and Data Science, Hebei University of Technology（人工智能与数据科学学院，河北工业大学）；China Xiongan Group Digital City Technology Company Ltd.（雄安集团数字城市技术有限公司）；State Key Laboratory of Information Security, Institute of Information Engineering, Chinese Academy of Sciences（信息安全国家重点实验室，信息工程研究所，中国科学院）；school of Instrumentation and Optoelectronic Engineering, Beihang University（仪器与光电工程学院，北航）
Road detection based on remote sensing images is of great significance to intelligent traffic management. The performances of the mainstream road detection methods are mainly determined by their extracted features, whose richness and robustness can be enhanced by fusing features of different types and cross-layer connections. However, the features in the existing mainstream model frameworks are often similar in the same layer by the single-task training, and the traditional cross-layer fusion ways are too simple to obtain an efficient effect, so more complex fusion ways besides concatenation a…
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University of Alabama at Birmingham（阿拉巴马大学伯明翰分校）；Cornell University（康奈尔大学）；Yale University（耶鲁大学）；University of California Irvine（加州大学伊万哥罗市分校）；Brown University（布朗大学）；Oak Ridge National Laboratory（橡树岭国家实验室）
Wuhan University（武汉大学）；University of Sydney（悉尼大学）；JD Explore Academy（京东探索研究院）
Road segmentation from remote sensing images is a challenging task with wide ranges of application potentials. Deep neural networks have advanced this field by leveraging the power of large-scale labeled data, which, however, are extremely expensive and time-consuming to acquire. One solution is to use cheap available data to train a model and deploy it to directly process the data from a specific application domain. Nevertheless, the well-known domain shift (DS) issue prevents the trained model from generalizing well on the target domain. In this paper, we propose a novel stagewise domain ada…
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Hong Kong University of Science and Technology, Guangzhou（香港科技大学（广州））；HKUST(GZ) - SmartMore Joint Lab（香港科技大学（广州）- 赛摩智能联合实验室）；SmartMore. Corp（赛摩智能公司）；Hong Kong University of Science and Technology（香港科技大学）
Shanghai Jiao Tong University（上海交通大学）；University of Leicester（莱斯特大学）；Central South University of Forestry and Technology（中南林业科技大学）；Massey University（梅西大学）
Road extraction in remote sensing images is of great importance for a wide range of applications. Because of the complex background, and high density, most of the existing methods fail to accurately extract a road network that appears correct and complete. Moreover, they suffer from either insufficient training data or high costs of manual annotation. To address these problems, we introduce a new model to apply structured domain adaption for synthetic image generation and road segmentation. We incorporate a feature pyramid network into generative adversarial networks to minimize the difference…
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School of Computer Science and Engineering, Sun Yat-sen University（中山大学计算机科学与工程学院）；Microsoft Research Asia（微软亚洲研究院）
The defect detection task can be regarded as a realistic scenario of object detection in the computer vision field and it is widely used in the industrial field. Directly applying vanilla object detector to defect detection task can achieve promising results, while there still exists challenging issues that have not been solved. The first issue is the texture shift which means a trained defect detector model will be easily affected by unseen texture, and the second issue is partial visual confusion which indicates that a partial defect box is visually similar with a complete box. To tackle the…
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Tsinghua University（清华大学）；Kolors Team, Kuaishou Technology（快手科技Kolors团队）；University of British Columbia（不列颠哥伦比亚大学）；Vector Institute（向量研究所）；South China Normal University（华南师范大学）
Graduate School of Fundamental Science and Engineering, Waseda University（早稻田大学基础科学与工程研究院）
This paper presents Capsule GAN, a Generative adversarial network using Capsule Network not only in the discriminator but also in the generator. Recently, Generative adversarial networks (GANs) has been intensively studied. However, generating images by GANs is difficult. Therefore, GANs sometimes generate poor quality images. These GANs use convolutional neural networks (CNNs). However, CNNs have the defect that the relational information between features of the image may be lost. Capsule Network, proposed by Hinton in 2017, overcomes the defect of CNNs. Capsule GAN reported previously uses C…
Automatic Road Subsurface Distress Recognition from Ground Penetrating Radar Images using Deep Learning-based Cross-verification
Department of Engineering Mechanics, School of Civil Engineering and Transportation, South China University of Technology（华南理工大学土木工程与交通学院力学系）；Guangdong Provincial Academy of Building Research Group Co. Ltd.（广东省建筑科学研究集团有限公司）；School of Civil and Transportation Engineering, Guangdong University of Technology（广东工业大学土木与交通学院）
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Rhineland-Palatinate Technical University of Kaiserslautern-Landau（凯撒斯劳滕-兰道技术大学）；German Research Center for Artificial Intelligence（德国人工智能研究中心）；Intelligentx GmbH；BiogentX；Department of Core Informatics, Graduate School of Informatics ,Osaka Metropolitan University（大阪 Metropolitan 大学信息学研究生院核心信息学系）
Hefei University of Technology（合肥工业大学）；Kingsoft Institute of Artificial Intelligence（金山人工智能研究院）
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Machine learning approaches for automatic defect detection in photovoltaic systems
Department of Electrical Engineering, Indian Institute of Technology Banaras Hindu University, Varanasi 221005, India（印度班加罗尔 Hindu 大学电子工程系）；Department of Mechanical Engineering, Texas Tech University, Lubbock, Texas 79409, USA（德克萨斯理工大学机械工程系）
Application of Segment Anything Model for Civil Infrastructure Defect Assessment
Florida Atlantic University（佛罗里达大西洋大学）；Missouri University of Science and Technology（密苏里科技大学）；University of Tehran（德黑兰大学）；University of Texas at Arlington（德克萨斯大学阿灵顿分校）；Boston University（波士顿大学）；University of Delaware（特拉华大学）；Florida International University（佛罗里达国际大学）
Deep generative models have shown success in generating 3D shapes with different representations. In this work, we propose Neural Volumetric Mesh Generator(NVMG) which can generate novel and high-quality volumetric meshes. Unlike the previous 3D generative model for point cloud, voxel, and implicit surface, the volumetric mesh representation is a ready-to-use representation in industry with details on both the surface and interior. Generating this such highly-structured data thus brings a significant challenge. We first propose a diffusion-based generative model to tackle this problem by gener…
