Abstract
The subject matter of this research article is automatic detection of pavement distress on highway roads using computer vision algorithms. Specifically, deep learning convolutional neural network models are employed towards the implementation of the detector. Source data for training the detector come in the form of orthoframes acquired by a mobile mapping system. Compared to our previous work, the orthoframes are generally of better quality, but more importantly, in this work, we introduce a manual preprocessing step: sets of orthoframes are carefully selected for training and manually digitized to ensure adequate performance of the detector. Pretrained convolutional neural networks are then fine-tuned for the problem of pavement distress detection. Corresponding experimental results are provided and analyzed and indicate a successful implementation of the detector.
Highlights
The condition of roads is one of the more important signs of economic standards and general well-being in a given country or region
We considered three architectures optimized for the ImageNet dataset for our task of pavement distress detection:
From the tests, it can be noted that the problem of crack detection benefited from the more sophisticated architecture of ConvNets as the 101 layer
Summary
The condition of roads is one of the more important signs of economic standards and general well-being in a given country or region. Efficient and timely road inspection is one of the key elements of a successful pavement management system. Periodical road surveys tend to be rather costly and time consuming if carried out in the traditional way, i.e., by human visual inspection of the road surface. Automatic image based road distress evaluation has become an option [1]. It is still an open research problem and subject to environmental conditions such as illumination level, shadows cast by nearby objects, etc., great progress has been made in this area, and various methods ranging from filtering and thresholding to artificial neural networks have been employed to carry out the task
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