The life-cycle monitoring of road health conditions is crucial for precise and automated maintenance of road infrastructure. Despite this importance, existing machine vision-based methods for identifying pavement dist...
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The life-cycle monitoring of road health conditions is crucial for precise and automated maintenance of road infrastructure. Despite this importance, existing machine vision-based methods for identifying pavement distresses are constrained in their susceptibleness to interference from factors such as shadows. To bridge this gap, the article introduces a novel hierarchical pavement structure monitoring model, which significantly enhances the task's performance. This proposed model integrates a de-shadowing module based on generative adversarial networks and an anchor-free instance segmentation model to provide pixel-level detection results, encompassing distress categories and morphological masks. A large-scale pavement image dataset was constructed, and a generative model was developed to expand pavement shadow samples. Experimental results show that the model achieved an average precision metric of 78.2%, surpassing existing baseline models. The effectiveness of the de-shadowing module was also validated through the significant improvements observed in various evaluation indicators. The proposed method demonstrates superior accuracy and robustness, holding promise for real-world road infrastructure monitoring systems.
Existing methods for substation meter reading rely on manual calibration to obtain the meter's type, pointer position, and range information. Utilizing the prior information and neural network methods, meter value...
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