A Deep Learning Approach for Surface Crack Classification and Segmentation in Unmanned Aerial Vehicle Assisted Infrastructure Inspections

Egodawela, Shamendra; Khodadadian Gostar, Amirali; Buddika, H. A. D. Samith; Dammika, A. J.; Harischandra, Nalin; Navaratnam, Satheeskumar; Mahmoodian, Mojtaba

doi:10.3390/s24061936

Cited by 4 publications

(1 citation statement)

References 45 publications

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“…According to the above analysis, we believe that bonding distance measurement based on 2D images is practicable. In recent years, computer vision techniques [ 6 , 7 , 8 , 9 ], which represent an important research area in deep learning [ 10 , 11 ], have developed rapidly. Many computer-vision-based methods have been applied in various fields, including aero-engine blade defect detection [ 12 ], steel plate defect inspection [ 13 ], and visual measurements [ 14 ].…”

Section: Introductionmentioning

confidence: 99%

Integrated Circuit Bonding Distance Inspection via Hierarchical Measurement Structure

Zhang,

Pu,

Zhang

et al. 2024

Sensors

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Bonding distance is defined by the projected distance on a substrate plane between two solder points of a bonding wire, which can directly affect the morphology of the bonding wire and the performance between internal components of the chip. For the inspection of the bonding distance, it is necessary to accurately recognize gold wires and solder points within the complex imagery of the chip. However, bonding wires at arbitrary angles and small-sized solder points are densely distributed across the complex background of bonding images. These characteristics pose challenges for conventional image detection and deep learning methods to effectively recognize and measure the bonding distances. In this paper, we present a novel method to measure bonding distance using a hierarchical measurement structure. First, we employ an image acquisition device to capture surface images of integrated circuits and use multi-layer convolution to coarsely locate the bonding region and remove redundant background. Second, we apply a multi-branch wire bonding inspection network for detecting bonding spots and segmenting gold wire. This network includes a fine location branch that utilizes low-level features to enhance detection accuracy for small bonding spots and a gold wire segmentation branch that incorporates an edge branch to effectively extract edge information. Finally, we use the bonding distance measurement module to develop four types of gold wire distribution models for bonding spot matching. Together, these modules create a fully automated method for measuring bonding distances in integrated circuits. The effectiveness of the proposed modules and overall framework has been validated through comprehensive experiments.

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Section: Introductionmentioning

confidence: 99%

Integrated Circuit Bonding Distance Inspection via Hierarchical Measurement Structure

Zhang,

Pu,

Zhang

et al. 2024

Sensors

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Innovative digital twin with artificial neural networks for real-time monitoring of structural response: A port structure case study

Jayasinghe,

Mahmoodian,

Sidiq

et al. 2024

Ocean Engineering

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Optimizing Concrete Crack Detection: An Attention-Based SWIN U-Net Approach

Sarhadi,

Ravanshadnia,

Monirabbasi

et al. 2024

IEEE Access

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Utilizing convolutional neural network (CNN) models, computer vision technology has become a reliable and powerful tool for detecting potential damage in concrete structures at the pixel level. In this study, an advanced SWIN U-Net architecture was introduced to detect concrete cracks. The model integrates attention-based convolutional neural networks to enhance the speed and accuracy of crack detection significantly. The distinctive features of the SWIN Transformer make the application of the model to images of varying sizes possible while the computational resources are used efficiently. To train the model, a dataset consisting of crack images, each accompanied by a corresponding mask that highlighted the relevant regions within the image, was used. The training data were augmented using Flip, Rotate, Random Contrast, Random Gamma, Random Brightness, Elastic Transformation, Grid Distortion, and Optical Distortion to counter potential overfitting. Additionally, L2 and spatial dropout regularization techniques were applied to the proposed model. The model was fine-tuned using stochastic gradient descent with the Adam optimizer, employing the binary cross-entropy loss function, and a learning rate of 0.001. The model was trained over 100 epochs with an adjustment scheduler. The performance of the model was evaluated using various metrics. Then, it was compared with three benchmarks and impressive results were achieved. Notably, the Dice loss and IOU values were 93% and 79%, respectively. The trained model had the exceptional performance score of 0.99 in accuracy, precision, recall, F1, and sensitivity.

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A Deep Learning Approach for Surface Crack Classification and Segmentation in Unmanned Aerial Vehicle Assisted Infrastructure Inspections

Cited by 4 publications

References 45 publications

Integrated Circuit Bonding Distance Inspection via Hierarchical Measurement Structure

Integrated Circuit Bonding Distance Inspection via Hierarchical Measurement Structure

Innovative digital twin with artificial neural networks for real-time monitoring of structural response: A port structure case study

Optimizing Concrete Crack Detection: An Attention-Based SWIN U-Net Approach

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