Noncontact deflection measurement for bridge through a multi‐UAVs system

Zhuge, Sheng; Xu, Xiangpeng; Zhong, Lijun; Gan, Shuwei; Lin, Bin; Yang, Xia; Zhang, Xiaohu

doi:10.1111/mice.12771

Cited by 35 publications

(22 citation statements)

References 46 publications

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“…But this will reduce the resolution of the target in the image, thus increasing the measurement error. Zhuge et al 61 developed a noncontact deflection measurement for the bridge through a multi-UAVs system. In this method, multiple UAVs equipped with cameras are used to measure the position to be measured and the fixed position of the bridge, respectively.…”

Section: Deflection Measurement Based On Uav Platformmentioning

confidence: 99%

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Measurement method and recent progress of vision-based deflection measurement of bridges: a technical review

et al. 2022

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The deflection that can reflect the vertical stiffness of a bridge plays an important role in the structural evaluation and health monitoring of bridges. In the past 20 years, the bridge deflection measurement methods based on computer vision and photogrammetry have been gradually applied to the field measurement due to the advantages of noncontact measurement, simple experimental setup, and easy installation. The technical research progress of vision-based bridge deflection measurement is reported from four aspects: basic principles, measurement methods, influencing factors, and applications. Basic principles mainly include camera calibration, three-dimensional (3D) stereo vision, photogrammetry, feature detection, and matching. For measurement methods, the single-camera two-dimensional measurement, the dual-camera 3D measurement, the quasistatic measurement based on photogrammetry, the multipoint dynamic measurement based on the displacement-relay videometrics and the deflection measurement based on UAV platform are introduced, respectively. In the section of influencing factors, this part summarizes the work of many researchers on the effects of camera imaging factors, calibration factors, algorithm factors, and environmental factors on measurement results. The field measurement results at different measurement distances and measurement accuracy based on these are presented in terms of applications. Finally, the future development trends of vision-based bridge deflection measurement are expected.

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Section: Deflection Measurement Based On Uav Platformmentioning

confidence: 99%

“…14 Deflection estimation for multi-UAV system. 61 the measured position relative to the bridge pier can be calculated. Therefore, the change of deflection Δh iþ1 i from t i to t iþ1 can be expressed as E Q -T A R G E T ; t e m p : i n t r a l i n k -; e 0 1 3 ; 1 1 6 ; 7 1 0…”

Section: Deflection Measurement Based On Uav Platformmentioning

confidence: 99%

Measurement method and recent progress of vision-based deflection measurement of bridges: a technical review

et al. 2022

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“…In practice, it is often necessary for the drone equipped with the camera to be close enough to the surface of the concrete structure to detect tiny cracks effectively. Usually, operating drones for bridge inspection is a complicated task and requires good operation skills (Zhuge et al., 2021). Considering the safety of the UAVs, the inspectors generally need to control a safe distance (usually over 1 m) between the UAV and the beam (Aldana‐Rodríguez et al., 2021).…”

Section: Introductionmentioning

confidence: 99%

Tiny‐Crack‐Net: A multiscale feature fusion network with attention mechanisms for segmentation of tiny cracks

Chu

Wang

Deng

2022

Computer aided Civil Eng

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Convolutional neural networks (CNNs) have gained growing interest in recent years for their advantages in detecting cracks on concrete bridge components. Class imbalance is a fundamental problem in crack segmentation, resulting in unsatisfactory segmentation for tiny cracks. Besides, limited by the local receptive field, CNNs often cannot integrate local features with global dependencies, thus significantly affecting the detection accuracy of tiny cracks across the entire image. To solve those problems in segmenting tiny cracks, a multiscale feature fusion network with attention mechanisms named “Tiny‐Crack‐Net” (TCN) is proposed. The modified residual network was used to capture the local features of tiny cracks. The dual attention module was then incorporated into the architecture to better separate the tiny cracks from the background. Also, a multiscale fusion operation was implemented to preserve the edge details of tiny cracks. Finally, a joint learning loss of the cross‐entropy and similarity was proposed to alleviate the poor convergence induced by the severe class imbalance of the pixels representing tiny cracks. The capability of the network in segmenting tiny cracks was remarkably enhanced by the aforementioned arrangements, and the “Tiny‐Crack‐Net” achieved a Dice similarity coefficient of 87.96% on an open‐source data set, which was at least 5.84% higher than those of the six cutting‐edge networks. The effectiveness and robustness of the “Tiny‐Crack‐Net” were validated with field test results, which showed that the intersection over union (IOU) for cracks with a width of 0.05 mm or wider reaches 91.44%.

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“…Lasers exhibit excellent penetrating power in the air and can stably project spots on the surface of objects over a distance. Some studies combined lasers with UAVs to measure structural displacement, where laser lights were attached to the UAVs for close‐up projection 35,36 . In this study, a highly penetrating laser lamp is fixed on the ground to project a laser spot onto the structure's surface, serving as a stationary reference point for the videos captured by the UAV.…”

Section: Introductionmentioning

confidence: 99%

Vision‐based displacement measurement using an unmanned aerial vehicle

Han

Feng

2022

Structural Contr & Hlth

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Vision‐based displacement measurement for structural health monitoring has gained popularity in recent years but still has several limitations in practical applications. Unmanned aerial vehicles (UAVs) provide opportunities to address the bottleneck problems of camera resolution insufficiency and mounting inconvenience due to their high maneuverability. However, existing methods using UAVs for structural displacement measurement are often complicated to operate due to the use of multiple stationary markers or multiple UAVs. This paper describes a novel vision‐based displacement measurement approach, using only one UAV, along with a motionless laser spot projected from a distance away as a reference. The positions of the marker and the laser spot are precisely calculated using a two‐step strategy, in which a designed black and white marker of known size is applied to the structure for scale definition and precise positioning. The adaptive region of interest (ROI) and adaptive binarization methods are utilized to improve the automatic applicability of the proposed approach with various background and brightness values. In this way, the motion of the UAV parallel and perpendicular to the plane of the structure can be eliminated by the stationary reference laser spot and the constantly updated scaling factors, respectively. The performance of the proposed method is validated on a two‐story frame and a suspension bridge. The results show that the displacement measured using the UAV agrees with the reference data obtained using the laser displacement sensor and the stationary camera, thereby demonstrating the accuracy and feasibility of the proposed method for displacement measurement for small‐ and large‐scale infrastructure.

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Noncontact deflection measurement for bridge through a multi‐UAVs system

Cited by 35 publications

References 46 publications

Measurement method and recent progress of vision-based deflection measurement of bridges: a technical review

Measurement method and recent progress of vision-based deflection measurement of bridges: a technical review

Tiny‐Crack‐Net: A multiscale feature fusion network with attention mechanisms for segmentation of tiny cracks

Vision‐based displacement measurement using an unmanned aerial vehicle

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