A Feasibility Study for Detection of Bridge Crack Based on UAV

이진환,; 김인호,; 정형조,

doi:10.5050/ksnve.2018.28.1.110

KSNVE

2018

DOI: 10.5050/ksnve.2018.28.1.110

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A Feasibility Study for Detection of Bridge Crack Based on UAV

이진환 이진환¹,

김인호 김인호²,

정형조 정형조³

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2024

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Hyperparameter Tuning Technique to Improve the Accuracy of Bridge Damage Identification Model

Chung,

Hong,

Kim

2024

Buildings

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In recent years, active research has been conducted using deep learning to evaluate damage to aging bridges. However, this method is inappropriate for practical use because its performance deteriorates owing to numerous classifications, and it does not use photos of actual sites. To this end, this study used image data from an actual bridge management system as training data and employed a combined learning model for each member among various instance segmentation models, including YOLO, Mask R-CNN, and BlendMask. Meanwhile, techniques such as hyperparameter tuning are widely used to improve the accuracy of deep learning, and this study aimed to improve the accuracy of the existing model through this. The hyperparameters optimized in this study are DEPTH, learning rate (LR), and iterations (ITER) of the neural network. This technique can improve the accuracy by tuning only the hyperparameters while using the existing model for bridge damage identification as it is. As a result of the experiment, when DEPTH, LR, and ITER were set to the optimal values, mAP was improved by approximately 2.9% compared to the existing model.

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Hyperparameter Tuning Technique to Improve the Accuracy of Bridge Damage Identification Model

Chung,

Hong,

Kim

2024

Buildings

View full text Add to dashboard Cite

show abstract

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A Feasibility Study for Detection of Bridge Crack Based on UAV

Cited by 1 publication

References 0 publications

Hyperparameter Tuning Technique to Improve the Accuracy of Bridge Damage Identification Model

Hyperparameter Tuning Technique to Improve the Accuracy of Bridge Damage Identification Model

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