提出混合模型，精准分割道路建筑裂缝，提升维护效率。
Hybrid-Segmentor: A Hybrid Approach to Automated Fine-Grained Crack Segmentation in Civil Infrastructure
- 融合编码器-解码器结构，同时捕捉裂缝局部与全局特征
- 在5项指标上超越现有模型，最高准确率达0.971
- 兼顾精度与计算效率，适合实际工程部署
检测与分割道路、建筑等基础设施中的裂缝对安全与低成本维护至关重要。尽管深度学习潜力巨大，但精确分割及应对多样裂缝类型仍面临挑战。本文提出新数据集与模型，旨在提升裂缝检测能力。我们引入Hybrid-Segmentor，一种基于编码器-解码器的混合方法，可同时提取细粒度局部与全局裂缝特征，增强模型对不同形状、表面和尺寸裂缝的泛化能力。为兼顾实用性与性能，我们在编码器中引入自注意力机制，同时简化解码器复杂度。该模型在5个量化指标上优于现有基准模型：准确率0.971，精确率0.804，召回率0.744，F1分数0.770，交并比0.630，达到当前最优水平。
原文摘要 · Abstract (English)
Detecting and segmenting cracks in infrastructure, such as roads and buildings, is crucial for safety and cost-effective maintenance. In spite of the potential of deep learning, there are challenges in achieving precise results and handling diverse crack types. With the proposed dataset and model, we aim to enhance crack detection and infrastructure maintenance. 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. To keep the computational performances low for practical purposes, while maintaining the high the generalization capabilities of the model, we incorporate a self-attention model at the encoder level, while reducing the complexity of the decoder component. 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.
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