Summary of Efficientcracknet: a Lightweight Model For Crack Segmentation, by Abid Hasan Zim et al.
EfficientCrackNet: A Lightweight Model for Crack Segmentation
by Abid Hasan Zim, Aquib Iqbal, Zaid Al-Huda, Asad Malik, Minoru Kuribayash
First submitted to arxiv on: 26 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A new computer vision model called EfficientCrackNet is designed to accurately detect cracks in pavement images. This is a challenging task due to various complexities such as intensity inhomogeneity, intricate topologies, low contrast, and noisy backgrounds. The proposed model combines Convolutional Neural Networks (CNNs) and transformers to segment cracks precisely. It incorporates depthwise separable convolutions (DSC) layers, MobileViT block, Edge Extraction Method (EEM), and Ultra-Lightweight Subspace Attention Module (ULSAM) for efficient crack edge detection without pretraining. Compared to existing lightweight models, EfficientCrackNet achieves superior performance on three benchmark datasets while requiring only 0.26M parameters and 0.483 FLOPs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary EfficientCrackNet is a new way to detect cracks in pavement pictures using computers. This helps keep buildings, roads, and bridges safe by finding problems early. Old methods didn’t work well because they were too slow or got confused by the images. The new model combines two types of computer learning (CNNs and transformers) to find cracks accurately. It’s good at finding edges in the pictures and doesn’t need to be trained on lots of data beforehand. |
Keywords
» Artificial intelligence » Attention » Pretraining
