Bottom-up image detection of water channel slope damages based on superpixel segmentation and support vector machine

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 super pixels 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.

Automated summary

This paper proposes a bottom-up image detection approach for slope damages, which effectively monitors the condition of slopes and achieves a high recognition accuracy of up to 91.7% through feature extraction and classification.