A Two-Stream Context-Aware ConvNet for Pavement Distress Detection
2020 43rd International Conference on Telecommunications and Signal Processing (TSP)(2020)
摘要
Convolutional neural networks (ConvNets) are widely used for pavement distress analysis tasks in which features are typically extracted from a smaller image (e.g. 224 × 224) that has been cropped from an orthophoto. This paper introduces a ConvNet-based method to classify partitioned segments of orthophotos for pavement distress by incorporating two image input streams, one of which provides contextual information of the segment's surroundings. The resulting two-stream ConvNet is evaluated to classify segments of the orthophoto with a better performance for pavement distress than single-stream ConvNets, in terms of precision and recall.
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关键词
Pavement distress,image processing,deep learning
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