AFFU-Net: Attention feature fusion U-Net with hybrid loss for winter jujube crack detection

Zheng, Zhouzhou; Hu, Yaohua; Yang, Huanbo; Qiao, Yichen; He, Yong; Zhang, Yan; Huang, Yuxiang

doi:10.1016/j.compag.2022.107049

Cited by 30 publications

(19 citation statements)

References 35 publications

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“…AFFU-Net: 65 This model is an attentional feature fusion network (AFFU-Net) based on the U-Net architecture, which has a hybrid loss and residual refinement module (RRM).…”

Section: Methodsmentioning

confidence: 99%

FSDGNet: frequency and spatial dual guide network for crack detection

Liu,

Yuan,

et al. 2024

J. Electron. Imag.

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Cracks are one of the major threats to the safe operation of civil infrastructures, so timely and accurate detection of cracks is crucial for accident prevention. However, in practical applications, the poor continuity and low contrast of many cracks (e.g., pavement cracks) pose a great challenge to image-based crack detection. In previous approaches, the detection results are often unsatisfactory due to the limitation of the dataset and the singularity of the detection method. To solve this problem, we propose a frequency and spatial dual guide network for crack detection. First, global frequency domain information is processed by the fast Fourier transform to extract low-level features, and then the convolutional neural network uses global frequency domain information to learn local features of the image to extract highlevel features. Finally, the feature maps of the two are weighted and fused element by element to obtain the final detection result. Extensive experiments show that our method is superior to any other existing method.

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“…AFFU-Net: 65 This model is an attentional feature fusion network (AFFU-Net) based on the U-Net architecture, which has a hybrid loss and residual refinement module (RRM).…”

Section: Methodsmentioning

confidence: 99%

FSDGNet: frequency and spatial dual guide network for crack detection

Liu,

Yuan,

et al. 2024

J. Electron. Imag.

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“…Scholars have done a lot of meaningful work in the field of crack image processing [19]. Luo et al proposed a finite state machine algorithm for image processing and trained a random forest classifier based on the crack image characteristics of artificially labeled samples.…”

Section: Related Workmentioning

confidence: 99%

A fine extraction algorithm for image-based surface cracks in underwater dams

Xin¹,

Fan²,

Shi³

et al. 2022

Meas. Sci. Technol.

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Under the action of water erosion and self-aging action, reservoir dams are prone to have cracks, affecting the safe operation. Underwater visual imaging can be used to detect dam surface cracks, but the spalling, aquatic plants and suspended sediments result in low image contrast and complex backgrounds. With the use of unsupervised machine learning, this paper proposes a fine segmentation and extraction algorithm for image-based dam surface cracks. Firstly, the adaptive histogram equalization is used to make the uneven illumination areas of underwater surface images be even illumination areas, whose statistical characteristics is calculated under the linear spatial filtering. Secondly, the extraction problem of interested crack areas after dodging preprocess is transformed into calculating the distance of image block cluster center, which can distinguish the image blocks of crack features from the background interference features. Thirdly, the fine extraction of crack images is carried out in consideration of the connected domains and morphological features, and the posterior probability of image sample category is obtained based on the soft clustering of Gaussian mixed model. Finally, different extraction algorithms related to surface cracks are evaluated in extensive experiments. The results validate the superior performance of the proposed extraction algorithm with 90.1% extraction accuracy, 6.5% missing alarm rate, and 7.2% false alarm rate.

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“…These methods also achieved certain results in the fields of defect detection and medical disease diagnosis [7][8][9][10]. Li et al [11] proposed a multi-resolution CNN for the diagnosis of PD in GIS, which proved the advantages of CNNs in the diagnosis of PD in GIS and gave the visualization results of the CNN.…”

Section: Introductionmentioning

confidence: 97%

Meta-autoencoder-based zero-shot learning for insulation defect diagnosis in gas-insulated switchgear

Wang

Yan²,

Yang

et al. 2023

Meas. Sci. Technol.

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Data-driven methods are the main methods of training models for diagnosis of insulation defects in gas-insulated switchgear (GIS). Because of complicated operating environments, sometimes no target samples are available for training, which leads to insufficient feature learning. Therefore, a meta-autoencoder based zero-shot learning method (MAZL) is proposed for diagnosis of GIS insulation defects. Firstly, visual features of insulation defects signals are extracted by a convolutional neural network. Then, the mapping between visual space and semantic space is learned by semantic autoencoder (SAE). For SAE, triplet loss is adapted to align semantic information, and unknown class semantic constraints are used to alleviate the domain offset. Finally, the nearest-neighbor algorithm is used to achieve zero-sample diagnosis. In model training, episode training is introduced to make MAZL model learn more discriminative semantic prototype, which alleviates classification deviations. The MAZL achieved a diagnostic accuracy for single and multi-source GIS insulation defects of 96.215% and 90.41% without using test class for training, respectively. This provides ideas for diagnosis of new GIS insulation defects.

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AFFU-Net: Attention feature fusion U-Net with hybrid loss for winter jujube crack detection

Cited by 30 publications

References 35 publications

FSDGNet: frequency and spatial dual guide network for crack detection

FSDGNet: frequency and spatial dual guide network for crack detection

A fine extraction algorithm for image-based surface cracks in underwater dams

Meta-autoencoder-based zero-shot learning for insulation defect diagnosis in gas-insulated switchgear

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