Automatic Detection of Cracks on Concrete Surfaces in the Presence of Shadows

Palevicius, Paulius; Pal, Mayur; Landauskas, Mantas; Orinaitė, Ugnė; Timofejeva, Inga; Ragulskis, Minvydas

doi:10.3390/s22103662

Cited by 29 publications

(19 citation statements)

References 29 publications

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“…For example, in [5], the authors proposed a vision-based method based on a deep convolutional neural network (CNN) for detecting concrete cracks, which can automatically learn image features without the need for other image processing techniques for feature extraction, compared to traditional image processing techniques. In [6], the authors developed a deep learningbased method for the automated processing of concrete surface images to perform crack recognition tasks and used shadow enhancement techniques to improve the accuracy of the automatic detection of cracks in concrete. In [7], the authors proposed a convolutional neural network called RUC-Net for pixel-level road crack segmentation and verified its effectiveness in road crack segmentation through experiments.…”

Section: Model Architecture Designmentioning

confidence: 99%

Efficient and Lightweight Spatial Attention-Based Shallow CNNs with Superior Performance for Concrete Surface Crack Detection

Dai,

Xia

2023

J. Phys.: Conf. Ser.

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To solve the problems of high training cost and low time efficiency when detecting concrete surface crack defects, this paper proposes a concrete surface crack detection method based on shallow CNNs. First, an image dataset of concrete surface cracks is constructed and preprocessing operations are performed on the dataset. Then, a shallow CNNs model for concrete surface crack detection is constructed, hyperparameters of the network model are set, spatial attention mechanism is introduced, and the dataset is input to the model for training. Finally, the results are analyzed and evaluated, and compared with the mainstream deep learning models InceptionV3 and Resnet50. Experimental results demonstrate that our proposed method achieves a 99.56% accuracy rate for detecting cracks on concrete surfaces, while also offering higher time efficiency and lower training costs. Given the significantly higher training costs associated with mainstream deep learning models such as InceptionV3 and ResNet50, our proposed method holds promise for future applications in industrial production processes and the intelligent field of construction quality inspection. By effectively, automatically, and accurately identifying cracks on concrete surfaces, it can reduce the risk of catastrophic failures.

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Section: Model Architecture Designmentioning

confidence: 99%

Efficient and Lightweight Spatial Attention-Based Shallow CNNs with Superior Performance for Concrete Surface Crack Detection

Dai,

Xia

2023

J. Phys.: Conf. Ser.

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“…The methodology based on external and embedded sensors is effective in crack detection [ 12 ]. Furthermore, data-driven approaches to solving complex downhole measurement problems were proposed [ 13 , 14 , 15 ]. All these research results are references for improving the downhole measurement in future.…”

Section: Introductionmentioning

confidence: 99%

Experimental Study on the Azimuthal-Transmitting Transducer of Acoustic Logging

Men

Che

2023

Sensors

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Azimuthal acoustic logging can survey the downhole formation more accurately, and the acoustic source is the crucial component of the downhole acoustic logging tool with azimuthal resolution characteristics. To realize downhole azimuthal detection, assembling multiple transmitting piezoelectric vibrators in the circumferential direction is necessary, and the performance of azimuthal-transmitting piezoelectric vibrators needs attention. However, effective heating test and matching methods are not yet developed for downhole multi-azimuth transmitting transducers. Therefore, this paper proposes an experimental method to comprehensively evaluate downhole azimuthal transmitters; furthermore, we analyze the azimuthal-transmitting piezoelectric vibrator parameters. This paper presents a heating test apparatus and studies the admittance and driving responses of the vibrator at different temperatures. The transmitting piezoelectric vibrators showing a good consistency in the heating test were selected, and an underwater acoustic experiment was performed. The main lobe angle of the radiation beam, horizontal directivity, and radiation energy of the azimuthal vibrators and azimuthal subarray are measured. The peak-to-peak amplitude radiated from the azimuthal vibrator and the static capacitance increase with an increase in temperature. The resonant frequency first increases and then decreases slightly with an increase in temperature. After cooling to room temperature, the parameters of the vibrator are consistent with those before heating. Hence, this experimental study can provide a foundation for the design and matching selection of azimuthal-transmitting piezoelectric vibrators.

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“…Shadows in images affect the accuracy of image segmentation, leading to low accuracy in computer vision tasks. For example, shadows can introduce ambiguity into concrete crack detection [1,2]. Accurately removing shadows can enhance the signal-to-noise ratio and, consequently, improve crack detection accuracy.…”

Section: Introductionmentioning

confidence: 99%

A dynamic alignment and illumination‐aware convolution for shadow removal

Wang,

Dai,

Chen

et al. 2023

IET Image Processing

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Shadow removal is a challenging task because the variety of shadows is influenced by surface texture and lighting. This paper proposes a dynamic alignment and illumination‐aware convolution (DAIC), which consists of a Feature Alignment Module (FAM) and a Dynamic Weight Module (DWM). FAM aligns the downsampled deep features with the original features and helps to extract the optimal local information to ensure that the object texture features are not corrupted. DWM generates weights according to different lighting variations for a better shadow removal result. The shadow removal approach is based on an image decomposition algorithm using a multi‐exposure image fusion model. Here, the shadow removal network and refinement network use U‐Net framework, and the transposed convolution operations are replaced with DAIC in the decoder part of U‐Net to improve the performance of the network. The experiments are conducted on two large shadow removal datasets, ISTD+ and SRD. Compared to state‐of‐the‐art methods, this model achieves optimal performance in terms of Root Mean Square Error (RMSE) for the non‐shadow region. It also achieves performance comparable to the state‐of‐the‐art method in terms of RMSE for the shadow region and structural similarity index measurement for the entire image.

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Automatic Detection of Cracks on Concrete Surfaces in the Presence of Shadows

Cited by 29 publications

References 29 publications

Efficient and Lightweight Spatial Attention-Based Shallow CNNs with Superior Performance for Concrete Surface Crack Detection

Efficient and Lightweight Spatial Attention-Based Shallow CNNs with Superior Performance for Concrete Surface Crack Detection

Experimental Study on the Azimuthal-Transmitting Transducer of Acoustic Logging

A dynamic alignment and illumination‐aware convolution for shadow removal

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