EfficientCrackNet: A Lightweight Model for Crack Segmentation (2409.18099v1)

Abstract: Crack detection, particularly from pavement images, presents a formidable challenge in the domain of computer vision due to several inherent complexities such as intensity inhomogeneity, intricate topologies, low contrast, and noisy backgrounds. Automated crack detection is crucial for maintaining the structural integrity of essential infrastructures, including buildings, pavements, and bridges. Existing lightweight methods often face challenges including computational inefficiency, complex crack patterns, and difficult backgrounds, leading to inaccurate detection and impracticality for real-world applications. To address these limitations, we propose EfficientCrackNet, a lightweight hybrid model combining Convolutional Neural Networks (CNNs) and transformers for precise crack segmentation. EfficientCrackNet integrates depthwise separable convolutions (DSC) layers and MobileViT block to capture both global and local features. The model employs an Edge Extraction Method (EEM) and for efficient crack edge detection without pretraining, and Ultra-Lightweight Subspace Attention Module (ULSAM) to enhance feature extraction. Extensive experiments on three benchmark datasets Crack500, DeepCrack, and GAPs384 demonstrate that EfficientCrackNet achieves superior performance compared to existing lightweight models, while requiring only 0.26M parameters, and 0.483 FLOPs (G). The proposed model offers an optimal balance between accuracy and computational efficiency, outperforming state-of-the-art lightweight models, and providing a robust and adaptable solution for real-world crack segmentation.

• The paper presents a hybrid CNN-transformer model combining UNet, MobileViT, and lightweight modules for efficient crack segmentation.
• It utilizes Depthwise Separable Convolutions, an innovative Edge Extraction Method, and an Ultra-Lightweight Subspace Attention Module to maintain high performance with only 0.26M parameters and 0.483 GFLOPs.
• Empirical validation on datasets like Crack500, DeepCrack, and GAPs384 shows significant improvements in precision and mIoU, highlighting its practical application for infrastructure inspection.

An Overview of EfficientCrackNet: A Lightweight Model for Crack Segmentation

The paper "EfficientCrackNet: A Lightweight Model for Crack Segmentation" presents a novel framework designed to address critical challenges in automated crack detection and segmentation, particularly for use in essential infrastructural contexts such as buildings, pavements, and bridges. This research introduces a hybrid approach that synergistically combines Convolutional Neural Networks (CNNs) with transformer architectures to enhance both the precision and computational efficiency required for effective crack segmentation.

Methodological Innovations

EfficientCrackNet is built on a UNet-based architecture that strategically integrates various advanced components, each playing a crucial role in the network's lightness and performance:

1. Depthwise Separable Convolutions (DSC): Employed throughout the architecture, these convolutions minimize computational cost and parameter count, thus making the model suitable for deployment on devices with limited resources.
2. MobileViT Block: Central to EfficientCrackNet's design, the MobileViT block efficiently encodes both local and global features, which are vital for accurate segmentation. This is achieved while maintaining a low parameter count, making it an optimal choice for mobile and resource-constrained applications.
3. Edge Extraction Method (EEM): A significant contribution of this paper, EEM integrates Difference of Gaussian (DoG) and Laplacian of Gaussian (LoG) methods with convolutional layers to enhance edge detection capabilities, crucial for segmenting fine crack structures without the need for additional pretraining data.
4. Ultra-Lightweight Subspace Attention Module (ULSAM): This module enhances feature extraction by focusing on multi-scale and cross-channel information, thereby improving the network's capacity to distinguish subtle crack features across varying spatial dimensions.

Performance Analysis

EfficientCrackNet is empirically validated against three benchmark datasets: Crack500, DeepCrack, and GAPs384. Across these datasets, the proposed model demonstrates superior performance in terms of precision and recall, achieving notable improvements in mean Intersection over Union (mIoU) compared to other leading lightweight models. With only 0.26M parameters and 0.483 GFLOPs, EfficientCrackNet provides a significant advantage in balancing accuracy and computational efficiency.

Implications and Future Directions

The implications of EfficientCrackNet extend both theoretically and practically. Theoretically, the integration of CNNs with transformer-based approaches in a lightweight model contributes to the evolving discourse on hybrid architectures for semantic segmentation tasks. Practically, the model's efficient design enables its application in real-world scenarios where computational resources are constrained, such as UAV-based structural inspections.

Future research can explore various avenues including the adaptation of the EfficientCrackNet framework for different types of structural defect detection beyond cracks, as well as experiments involving more diverse environmental conditions to further validate its robustness. Additionally, enhancing the model's adaptability to detect extremely thin cracks remains an ongoing challenge that future iterations could aim to address.

In conclusion, EfficientCrackNet represents a pivotal step towards the development of practical, efficient AI solutions for infrastructure maintenance, offering a model that not only meets the accuracy demands but is also equipped for deployment in real-world, resource-constrained environments.