Robust and Generalizable Crack Segmentation in Civil Infrastructures using a Fine-tuned Vision Foundation Model

Основные понятия

A vision foundation model, the Segment Anything Model (SAM), can be effectively fine-tuned using parameter-efficient methods to achieve robust and generalizable crack segmentation performance, outperforming state-of-the-art models.

Аннотация

This study introduces the use of a vision foundation model, the Segment Anything Model (SAM), for crack segmentation in civil infrastructures. Two parameter-efficient fine-tuning (PEFT) methods, adapter and low-rank adaptation (LoRA), are employed to fine-tune the SAM for this task.
The authors collected two unique datasets, Road420 and Facade390, to evaluate the zero-shot performance of the fine-tuned CrackSAM model on previously unseen data. Comparative experiments were conducted with twelve state-of-the-art semantic segmentation models.
The results show that the CrackSAM model exhibits outstanding performance on datasets with artificial noise (e.g., low lighting, low resolution) and superior cross-dataset generalization compared to the other models. CrackSAM demonstrates remarkable robustness to challenging conditions such as shadows, road markings, construction joints, and other interference factors.
The authors also propose deployment strategies for the CrackSAM model, including cloud computing and knowledge distillation-based edge computing, to address the high computational requirements of foundation models.
The key contributions of this work are:

Applying PEFT methods to fine-tune the SAM foundation model for crack segmentation.
Evaluating the zero-shot performance of the fine-tuned CrackSAM on previously unseen datasets.
Demonstrating the superior robustness and generalization capabilities of CrackSAM compared to state-of-the-art models.
Proposing deployment strategies to enable practical implementation of the CrackSAM model.

Статистика

The authors collected two unique datasets, Road420 and Facade390, to evaluate the zero-shot performance of the fine-tuned CrackSAM model on previously unseen data.

Цитаты

"CrackSAM exhibits remarkable superiority, particularly under challenging conditions such as dim lighting, shadows, road markings, construction joints, and other interference factors."
"The advantage of CrackSAM lies in its powerful zero-shot capability, demonstrated by its robustness to noise and superior cross-dataset generalization compared to traditional classic architectures."

Ключевые выводы из

Fine-tuning vision foundation model for crack segmentation in civil infrastructures

by Kang Ge,Chen... в arxiv.org 04-24-2024

https://arxiv.org/pdf/2312.04233.pdf

Дополнительные вопросы

How can the CrackSAM model be further improved to reduce its computational requirements and enable deployment on resource-constrained edge devices?

To reduce the computational requirements of the CrackSAM model and enable deployment on resource-constrained edge devices, several strategies can be implemented:

Model Compression Techniques: Utilize model compression techniques such as quantization, pruning, and distillation to reduce the model size and computational complexity while maintaining performance. This can help in making the model more lightweight and suitable for deployment on edge devices.

Architecture Optimization: Explore more efficient architectures or design custom architectures tailored for edge deployment. This could involve simplifying the network structure, reducing the number of parameters, or optimizing the model for faster inference.

Hardware Acceleration: Utilize hardware accelerators like GPUs, TPUs, or specialized edge AI chips to improve the model's performance on edge devices. Hardware acceleration can significantly speed up inference and reduce computational requirements.

Selective Fine-Tuning: Fine-tune the model on specific edge device data to adapt it to the device's constraints and requirements. This targeted fine-tuning can help optimize the model for deployment on edge devices.

Dynamic Inference: Implement dynamic inference techniques that adjust the model's complexity based on the available resources during runtime. This adaptive approach can optimize performance while meeting the constraints of edge devices.

By implementing these strategies, the CrackSAM model can be optimized for deployment on resource-constrained edge devices, making it more accessible and practical for real-world applications in civil engineering.

What other types of civil infrastructure defects, beyond cracks, could the CrackSAM model be fine-tuned to detect and segment?

The CrackSAM model, with its robust segmentation capabilities, can be fine-tuned to detect and segment various other types of civil infrastructure defects, including:

Corrosion: Detecting and segmenting corrosion spots on metal structures such as bridges, pipelines, and buildings.

Spalling: Identifying and segmenting areas of concrete spalling or deterioration on structures like bridges, parking garages, and tunnels.

Delamination: Detecting and segmenting delamination in concrete structures, which can indicate structural integrity issues.

Joint Seal Damage: Identifying and segmenting damaged joint seals in pavements, bridges, and buildings to prevent water ingress and structural damage.

Rebar Exposure: Detecting and segmenting areas where reinforcement bars are exposed in concrete structures, indicating potential durability issues.

Crushed Aggregate Detection: Segmenting areas with crushed or degraded aggregates in asphalt pavements, which can impact the road's performance.

By fine-tuning the CrackSAM model to detect and segment these additional civil infrastructure defects, it can enhance structural health monitoring efforts, improve maintenance practices, and ensure the longevity and safety of critical infrastructure assets.

How can the knowledge gained from fine-tuning the CrackSAM model be leveraged to develop more efficient and generalizable computer vision models for other civil engineering applications?

The knowledge gained from fine-tuning the CrackSAM model can be leveraged to develop more efficient and generalizable computer vision models for other civil engineering applications through the following approaches:

Transfer Learning: Apply transfer learning techniques to transfer the knowledge gained from fine-tuning CrackSAM to other computer vision tasks in civil engineering. Pretrained weights, architectures, and fine-tuning strategies can be adapted to new applications.

Dataset Augmentation: Use the annotated datasets and expertise acquired during the fine-tuning of CrackSAM to augment and curate datasets for other civil engineering applications. This enriched data can improve model performance and generalization.

Algorithmic Improvements: Implement algorithmic improvements and best practices identified during the fine-tuning process of CrackSAM in developing new computer vision models. This includes strategies for robustness, generalization, and efficient inference.

Collaborative Research: Collaborate with domain experts in different civil engineering domains to tailor the knowledge gained from CrackSAM fine-tuning to specific applications. This interdisciplinary approach can lead to more effective and specialized models.

Continuous Learning: Continuously update and refine the models based on feedback from real-world deployments and new data. This iterative process of learning and improvement can enhance the efficiency and adaptability of computer vision models in civil engineering.

By leveraging the knowledge and experience gained from fine-tuning CrackSAM, developers can build more efficient, accurate, and generalizable computer vision models for a wide range of civil engineering applications, contributing to improved infrastructure monitoring and maintenance practices.