DSCformer: A Dual-Branch Network Integrating Enhanced Dynamic Snake Convolution and SegFormer for Crack Segmentation

• URL: http://arxiv.org/abs/2411.09371v1
• Date: Thu, 14 Nov 2024 11:25:32 GMT
• Title: DSCformer: A Dual-Branch Network Integrating Enhanced Dynamic Snake Convolution and SegFormer for Crack Segmentation
• Abstract summary: Current convolutional neural networks (CNNs) have demonstrated strong performance in crack segmentation tasks. Transformers excel at capturing global context but lack precision in detailed feature extraction. We introduce DSCformer, a novel hybrid model that integrates an enhanced Dynamic Snake Convolution (DSConv) with a Transformer architecture for crack segmentation.
• Score: 6.898227391740093
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In construction quality monitoring, accurately detecting and segmenting cracks in concrete structures is paramount for safety and maintenance. Current convolutional neural networks (CNNs) have demonstrated strong performance in crack segmentation tasks, yet they often struggle with complex backgrounds and fail to capture fine-grained tubular structures fully. In contrast, Transformers excel at capturing global context but lack precision in detailed feature extraction. We introduce DSCformer, a novel hybrid model that integrates an enhanced Dynamic Snake Convolution (DSConv) with a Transformer architecture for crack segmentation to address these challenges. Our key contributions include the enhanced DSConv through a pyramid kernel for adaptive offset computation and a simultaneous bi-directional learnable offset iteration, significantly improving the model's performance to capture intricate crack patterns. Additionally, we propose a Weighted Convolutional Attention Module (WCAM), which refines channel attention, allowing for more precise and adaptive feature attention. We evaluate DSCformer on the Crack3238 and FIND datasets, achieving IoUs of 59.22\% and 87.24\%, respectively. The experimental results suggest that our DSCformer outperforms state-of-the-art methods across different datasets.

Related papers

• SCRWKV: Ultra-Compact Structure-Calibrated Vision-RWKV for Topological Crack Segmentation [36.61448234263959]
  We propose the Ultra-Compact Structure-Calibrated Vision RWKV (SCRWKV) to achieve pixel-level accurate segmentation of structural cracks.<n>The network achieves high-precision modeling via a novel Structure-Field backbone while maintaining linear complexity.<n>We show that SCRWKV, with only 1.22M parameters, significantly outperforms SOTA methods.
  arXiv  Detail & Related papers  (2026-05-14T15:01:22Z)
• A Streamlined Attention-Based Network for Descriptor Extraction [12.162883531502173]
  We introduce SANDesc, a Streamlined Attention-Based Network for Descriptor extraction.<n>We employ a revised U-Net-like architecture enhanced with Convolutional Block Attention Modules and residual paths.<n>Sandesc achieves substantial performance gains over the existing descriptors while operating with limited computational resources.
  arXiv  Detail & Related papers  (2026-01-19T15:12:42Z)
• Equivariant-Aware Structured Pruning for Efficient Edge Deployment: A Comprehensive Framework with Adaptive Fine-Tuning [0.0]
  We present a framework combining group equivariant convolutional neural networks (G-CNNs) with equivariant-aware structured pruning.<n>Our approach preserves equivariant properties by analyzing e2cnn layer structure and applying neuron-level pruning to fully connected components.<n>We evaluate our method on satellite imagery (EuroSAT) and standard benchmarks (CIFAR-10, Rotated MNIST) demonstrating effectiveness across diverse domains.
  arXiv  Detail & Related papers  (2025-11-21T13:41:47Z)
• SCas4D: Structural Cascaded Optimization for Boosting Persistent 4D Novel View Synthesis [53.10680153186481]
  We propose SCas4D, a cascaded optimization framework that leverages structural patterns in 3D Gaussian Splatting for dynamic scenes.<n>By progressively refining deformations from coarse part-level to fine point-level, SCas4D achieves convergence within 100 iterations per time frame.<n>The approach also demonstrates effectiveness in self-supervised articulated object segmentation, novel view synthesis, and dense point tracking tasks.
  arXiv  Detail & Related papers  (2025-10-08T06:39:33Z)
• Deep Equilibrium Convolutional Sparse Coding for Hyperspectral Image Denoising [16.405355853358202]
  Hyperspectral images (HSIs) play a crucial role in remote sensing but are often degraded by complex noise patterns.<n> Ensuring the physical property of the denoised HSIs is vital for robust HSI denoising, giving the rise of deep unfolding-based methods.<n>We propose a Deep Equilibrium Convolutional Sparse Coding (DECSC) framework that unifies local spatial-spectral correlations, nonlocal spatial self-similarities, and global spatial consistency.
  arXiv  Detail & Related papers  (2025-08-21T13:35:11Z)
• BHViT: Binarized Hybrid Vision Transformer [53.38894971164072]
  Model binarization has made significant progress in enabling real-time and energy-efficient computation for convolutional neural networks (CNN) We propose BHViT, a binarization-friendly hybrid ViT architecture and its full binarization model with the guidance of three important observations. Our proposed algorithm achieves SOTA performance among binary ViT methods.
  arXiv  Detail & Related papers  (2025-03-04T08:35:01Z)
• Transformer Meets Twicing: Harnessing Unattended Residual Information [2.1605931466490795]
  Transformer-based deep learning models have achieved state-of-the-art performance across numerous language and vision tasks. While the self-attention mechanism has proven capable of handling complex data patterns, it has been observed that the representational capacity of the attention matrix degrades significantly across transformer layers. We propose the Twicing Attention, a novel attention mechanism that uses kernel twicing procedure in nonparametric regression to alleviate the low-pass behavior of associated NLM smoothing.
  arXiv  Detail & Related papers  (2025-03-02T01:56:35Z)
• S3TU-Net: Structured Convolution and Superpixel Transformer for Lung Nodule Segmentation [5.2752693301728355]
  We propose a segmentation model, S3TU-Net, which integrates multi-dimensional spatial connectors and a superpixel-based visual transformer. S3TU-Net is built on a multi-view CNN-Transformer hybrid architecture, incorporating superpixel algorithms, structured weighting, and spatial shifting techniques. Experimental results on the LIDC-IDRI dataset demonstrate that S3TU-Net achieves a DSC, precision, and IoU of 89.04%, 90.73%, and 90.70%, respectively.
  arXiv  Detail & Related papers  (2024-11-19T15:00:18Z)
• Topology-aware Mamba for Crack Segmentation in Structures [5.9184143707401775]
  CrackMamba, a Mamba-based model, is designed for efficient and accurate crack segmentation for monitoring the structural health of infrastructure. CrackMamba addresses these challenges by utilizing the VMambaV2 with pre-trained ImageNet-1k weights as the encoder and a newly designed decoder for better performance. Experiments show that CrackMamba achieves state-of-the-art (SOTA) performance on the CrackSeg9k and SewerCrack datasets, and demonstrates competitive performance on the retinal vessel segmentation dataset CHASEunderlineDB1.
  arXiv  Detail & Related papers  (2024-10-25T15:17:52Z)
• Hybrid-Segmentor: A Hybrid Approach to Automated Fine-Grained Crack Segmentation in Civil Infrastructure [52.2025114590481]
  We introduce Hybrid-Segmentor, an encoder-decoder based approach that is capable of extracting both fine-grained local and global crack features. This allows the model to improve its generalization capabilities in distinguish various type of shapes, surfaces and sizes of cracks. The proposed model outperforms existing benchmark models across 5 quantitative metrics (accuracy 0.971, precision 0.804, recall 0.744, F1-score 0.770, and IoU score 0.630), achieving state-of-the-art status.
  arXiv  Detail & Related papers  (2024-09-04T16:47:16Z)
• Embracing Events and Frames with Hierarchical Feature Refinement Network for Object Detection [17.406051477690134]
  Event cameras output sparse and asynchronous events, providing a potential solution to solve these problems. We propose a novel hierarchical feature refinement network for event-frame fusion. Our method exhibits significantly better robustness when introducing 15 different corruption types to the frame images.
  arXiv  Detail & Related papers  (2024-07-17T14:09:46Z)
• Coarse-Fine Spectral-Aware Deformable Convolution For Hyperspectral Image Reconstruction [15.537910100051866]
  We study the inverse problem of Coded Aperture Snapshot Spectral Imaging (CASSI) We propose Coarse-Fine Spectral-Aware Deformable Convolution Network (CFSDCN) Our CFSDCN significantly outperforms previous state-of-the-art (SOTA) methods on both simulated and real HSI datasets.
  arXiv  Detail & Related papers  (2024-06-18T15:15:12Z)
• CFPFormer: Feature-pyramid like Transformer Decoder for Segmentation and Detection [1.837431956557716]
  We propose Cross Feature Pyramid Transformer decoder (CFPFormer), a novel decoder block that integrates feature pyramids and transformers. Our work is capable of capturing long-range dependencies and effectively up-sample feature maps. With a ResNet50 backbone, our method achieves 92.02% Dice Score, highlighting the efficacy of our methods.
  arXiv  Detail & Related papers  (2024-04-23T18:46:07Z)
• TCCT-Net: Two-Stream Network Architecture for Fast and Efficient Engagement Estimation via Behavioral Feature Signals [58.865901821451295]
  We present a novel two-stream feature fusion "Tensor-Convolution and Convolution-Transformer Network" (TCCT-Net) architecture. To better learn the meaningful patterns in the temporal-spatial domain, we design a "CT" stream that integrates a hybrid convolutional-transformer. In parallel, to efficiently extract rich patterns from the temporal-frequency domain, we introduce a "TC" stream that uses Continuous Wavelet Transform (CWT) to represent information in a 2D tensor form.
  arXiv  Detail & Related papers  (2024-04-15T06:01:48Z)
• S^2Former-OR: Single-Stage Bi-Modal Transformer for Scene Graph Generation in OR [50.435592120607815]
  Scene graph generation (SGG) of surgical procedures is crucial in enhancing holistically cognitive intelligence in the operating room (OR) Previous works have primarily relied on multi-stage learning, where the generated semantic scene graphs depend on intermediate processes with pose estimation and object detection. In this study, we introduce a novel single-stage bi-modal transformer framework for SGG in the OR, termed S2Former-OR.
  arXiv  Detail & Related papers  (2024-02-22T11:40:49Z)
• SCHEME: Scalable Channel Mixer for Vision Transformers [52.605868919281086]
  Vision Transformers have achieved impressive performance in many vision tasks. Much less research has been devoted to the channel mixer or feature mixing block (FFN or) We show that the dense connections can be replaced with a diagonal block structure that supports larger expansion ratios.
  arXiv  Detail & Related papers  (2023-12-01T08:22:34Z)
• Generalized Face Forgery Detection via Adaptive Learning for Pre-trained Vision Transformer [54.32283739486781]
  We present a textbfForgery-aware textbfAdaptive textbfVision textbfTransformer (FA-ViT) under the adaptive learning paradigm. FA-ViT achieves 93.83% and 78.32% AUC scores on Celeb-DF and DFDC datasets in the cross-dataset evaluation.
  arXiv  Detail & Related papers  (2023-09-20T06:51:11Z)
• A Convolutional-Transformer Network for Crack Segmentation with Boundary Awareness [5.98717173705421]
  Cracks play a crucial role in assessing the safety and durability of manufactured buildings. We propose a novel convolutional-transformer network based on encoder-decoder architecture to solve this challenge.
  arXiv  Detail & Related papers  (2023-02-23T01:27:57Z)
• CSformer: Bridging Convolution and Transformer for Compressive Sensing [65.22377493627687]
  This paper proposes a hybrid framework that integrates the advantages of leveraging detailed spatial information from CNN and the global context provided by transformer for enhanced representation learning. The proposed approach is an end-to-end compressive image sensing method, composed of adaptive sampling and recovery. The experimental results demonstrate the effectiveness of the dedicated transformer-based architecture for compressive sensing.
  arXiv  Detail & Related papers  (2021-12-31T04:37:11Z)
• Structured Convolutions for Efficient Neural Network Design [65.36569572213027]
  We tackle model efficiency by exploiting redundancy in the textitimplicit structure of the building blocks of convolutional neural networks. We show how this decomposition can be applied to 2D and 3D kernels as well as the fully-connected layers.
  arXiv  Detail & Related papers  (2020-08-06T04:38:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.