The operation of water supply channels is threatened by the occasionally occurred slope damages. Timely detection of their occurrence is critical for the rapid enforcement of mitigation measures. However, current practices based on routine inspection and structural heath monitoring are inefficient, laborious and tend to be biased. As an attempt to address the limitations, this paper proposes a bottom-up image detection approach for slope damages, which includes four steps, i.e. superpixel segmentation, feature handcrafting, superpixel classification based on support vector machine (SVM), and slope damage recognition. The approach employs a bottom-up strategy to infer the upper-level slope condition from the classification results of individual superpixels in the bottom level. Experiments were conducted to demonstrate the effectiveness of the approach. The handcrafted feature “LBP + HSV” was demonstrated to be effective in characterizing the image features of slope damages. An SVM model with “LBP + HSV” as input can reliably identify the slope condition in superpixels. Based on the SVM model, the bottom-up strategy achieved high recognition performance, of which the overall accuracy can be up to 91.7%. The proposed approach has potential to facilitate the early and comprehensive awareness of slope damages along the entire route of water channel by the integration with unmanned aerial vehicles.

偶尔发生的边坡破坏会威胁到供水渠道的运行。及时发现它们的发生对于快速执行缓解措施至关重要。然而，基于常规检查和结构健康监测的当前实践效率低下，费力且容易产生偏差。为了解决这些局限性，本文提出了一种自底向上的斜坡损伤图像检测方法，该方法包括四个步骤，即超像素分割，特征手工制作，基于支持向量机（SVM）的超像素分类以及斜坡损伤识别。该方法采用了自下而上的策略，可以根据底层单个超像素的分类结果来推断上层倾斜条件。进行实验以证明该方法的有效性。手工制作的特征“ LBP + HSV”被证明可以有效地表征斜坡损伤的图像特征。以“ LBP + HSV”作为输入的SVM模型可以可靠地识别超像素的斜率条件。在支持向量机模型的基础上，自下而上的策略实现了较高的识别性能，其总体准确率高达91.7％。通过与无人飞行器集成，该方法具有促进早期和全面认识整个水道路径上的坡度破坏的潜力。在支持向量机模型的基础上，自下而上的策略实现了较高的识别性能，其总体准确率高达91.7％。通过与无人飞行器集成，该方法具有促进早期和全面认识整个水道路径上的坡度破坏的潜力。在支持向量机模型的基础上，自下而上的策略实现了较高的识别性能，其总体准确率高达91.7％。通过与无人飞行器集成，该方法具有促进早期和全面认识整个水道路径上的坡度破坏的潜力。