PTZ Camera-Based Image Processing for Automatic Crack Size Measurement in Expressways

Shao, Chunyan; Zhang, Limin; Wang, Pan

doi:10.1109/jsen.2021.3112005

Cited by 10 publications

(6 citation statements)

References 20 publications

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“…Moreover, one-stage models [8][9][10][11][12][13][14] treat object detection as a regression task, directly predicting bounding boxes and categories across the image. YOLO-based detection models [15][16][17][18][19][20][21][22][23][24][25][26][27][28] have been studied for real-time detection. Yu et al [27] proposed a multisource domain adaptation model for crack detection that uses ensembled labels to realign source and target domains.…”

Section: Crack Detectionmentioning

confidence: 99%

Auxcoformer: Auxiliary and Contrastive Transformer for Robust Crack Detection in Adverse Weather Conditions

Yoon,

Jung,

Yoo

2024

Mathematics

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Crack detection is integral in civil infrastructure maintenance, with automated robots for detailed inspections and repairs becoming increasingly common. Ensuring fast and accurate crack detection for autonomous vehicles is crucial for safe road navigation. In these fields, existing detection models demonstrate impressive performance. However, they are primarily optimized for clear weather and struggle with occlusions and brightness variations in adverse weather conditions. These problems affect automated robots and autonomous vehicle navigation that must operate reliably in diverse environmental conditions. To address this problem, we propose Auxcoformer, designed for robust crack detection in adverse weather conditions. Considering the image degradation caused by adverse weather conditions, Auxcoformer incorporates an auxiliary restoration network. This network efficiently restores damaged crack details, ensuring the primary detection network obtains better quality features. The proposed approach uses a non-local patch-based 3D transform technique, emphasizing the characteristics of cracks and making them more distinguishable. Considering the connectivity of cracks, we also introduce contrastive patch loss for precise localization. Then, we demonstrate the performance of Auxcoformer, comparing it with other detection models through experiments.

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Section: Crack Detectionmentioning

confidence: 99%

Auxcoformer: Auxiliary and Contrastive Transformer for Robust Crack Detection in Adverse Weather Conditions

Yoon,

Jung,

Yoo

2024

Mathematics

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“…Mobile device‐based highway pavement crack detection [1] suffers from an absence of distance measurement, requires light traffic flow, is extremely expensive, and is difficult to use in real‐world engineering applications. Owing to the rapid development of intelligent transportation system (ITS) technology in China, a fixed PTZ vision‐based low‐cost highway pavement crack detection method was proposed to achieve effective and practical crack detection [2]. However, owing to the limitations of PTZ camera distances (CDs), the quality of the crack images captured under far CDs decreases, resulting in unclear boundaries within the crack patches, as illustrated in Figure 1.…”

Section: Introductionmentioning

confidence: 99%

Step‐by‐step image enhancement method for PTZ‐camera based crack detection in expressways

Xia,

Shao,

Feng

et al. 2024

Electronics Letters

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Cracks on highway pavement surfaces are among the most critical problems affecting expressway maintenance. A pan/tilt/zoom (PTZ) camera‐based crack detection method was proposed, owing to the intelligent traffic system (ITS) development of an expressway network in China. However, the quality of the crack image decreased at distant PTZ camera distances, resulting in a low mean average precision (MVP) for crack detection, which is difficult to implement in civil engineering. A weighted superposition‐based crack enhancement algorithm for crack detection was proposed to enhance crack images collected by PTZ cameras. Compared with the poor generalization ability of using a single image enhancement method, this method combines the advantages and characteristics of multiple image enhancement methods, thereby ensuring the quality of crack images. This enhances the visual characteristics of the cracks to adapt to various illuminations, thereby improving the crack detection rate and providing scientific and technological reserves for its application in highway maintenance engineering. Further comparison tests on crack detection for pavement crack images collected from the China G4/Jingshi Highway demonstrated that the proposed method was efficient in detecting is efficiently to detect cracks, achieving 96% MAP and 94% F1 values after enhancing the crack images, thereby providing an objective and promising means of crack detection using PTZ cameras.

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“…Among them, with the popularization and application of computers, face recognition in image recognition has been used in various fields [4]. Face recognition, as a typical recognition of three-dimensional natural objects, has an important theoretical value, and how to describe and represent the face is an important content of face recognition research, which has an important application value for image compression and information transmission [5][6].…”

Section: Introductionmentioning

confidence: 99%

“…With the continuous development of artificial intelligence, the Internet of Things, and other cutting-edge technologies, the design of the smart campus, smart city, smart health care, and other fields are becoming more and more sophisticated, especially since 2020, driven by the rigid demand for epidemic prevention and control, the application of face recognition technology in the whole society is constantly strengthened [8][9][10]. The use of face recognition technology in society is a very important part of the research on how to describe and represent the face, which has an important application value for image compression and information transmission [5][6]. The use of an advanced face recognition system can not only improve the efficiency of management but also bring public safety and security and the convenience of life, work, and study [11].…”

Section: Introductionmentioning

confidence: 99%

Research on the design of automatic image processing function for intelligent face management system

Cai

2024

Applied Mathematics and Nonlinear Sciences

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Around the background of rapid development of intelligent technology, an efficient image processing system oriented to the smart management of human faces is focused on. The system is mainly developed towards high speed, high definition, high integration and reliability. The article investigates a novel automatic image processing algorithm, covering three key modules: automatic exposure control, color interpolation and chromaticity space conversion. The algorithm can process the output image of CMOS sensor in Bayer format in real time and adjust the image parameters to obtain a high-quality image. In terms of face recognition performance, the algorithm has a significant advantage in recognition speed compared with other algorithms, and the average recognition accuracy reaches 94.258%. In the practical application of ID card portrait processing, the image shows a more uniform grayscale distribution in the range of 5 to 255 after automatic color adjustment, and the color quality is significantly improved. Meanwhile, in the portrait enhancement experiments, the images obtained with this image automatic processing algorithm outperform the traditional ID card image processing methods regarding information entropy, mutual information, standard deviation and peak signal-to-noise ratio (PSNR).

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PTZ Camera-Based Image Processing for Automatic Crack Size Measurement in Expressways

Cited by 10 publications

References 20 publications

Auxcoformer: Auxiliary and Contrastive Transformer for Robust Crack Detection in Adverse Weather Conditions

Auxcoformer: Auxiliary and Contrastive Transformer for Robust Crack Detection in Adverse Weather Conditions

Step‐by‐step image enhancement method for PTZ‐camera based crack detection in expressways

Research on the design of automatic image processing function for intelligent face management system

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