Accurate, rapid, and automated landslide detection is crucial for early warning, emergency management, and landslide mechanism analysis. Increasingly general-purpose detection models are being deployed for these complex and dynamic tasks involving features that are difficult to characterize. However, these models are computationally expensive and memory-hungry, while the accuracy and detection efficiency remain wanting. To address the above problems, this paper proposes an end-to-end model with high-precision and lightweight design for integrated landslide detection and segmentation. Here, we customized the backbone utilizing the advanced Efficient MOdel (EMO), and further used the linear cheap operation from GhostNet to reduce computational complexity. As a result, the total parameters of our models were reduced by up to 48.13%, compared to the baseline. Building on this, we employed a dynamic detection head with multiple attention mechanisms, and proposed a lightweight attention enhancement module for strengthened multi-scale feature extraction and fusion. The results demonstrate that our model outperforms the baseline on all metrics, achieving an outstanding F1 score of 96.75%.

中文翻译：

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使用轻量级方法从卫星图像中检测滑坡


准确、快速和自动化的滑坡检测对于早期预警、应急管理和滑坡机制分析至关重要。越来越多的通用检测模型被部署用于这些涉及难以表征的特征的复杂和动态任务。然而，这些模型计算成本高昂且内存消耗大，而准确性和检测效率仍然不足。针对上述问题，本文提出了一种高精度、轻量化设计的端到端滑坡检测与分割一体化模型。在这里，我们利用先进的高效模块 （EMO） 定制了主干网，并进一步使用了 GhostNet 的线性廉价运算来降低计算复杂性。结果，与基线相比，我们模型的总参数减少了 48.13%。在此基础上，我们采用了具有多种注意力机制的动态检测头，并提出了一种轻量级的注意力增强模块，用于加强多尺度特征提取和融合。结果表明，我们的模型在所有指标上都优于基线，取得了 96.75% 的出色 F1 分数。