Deterioration of road and pavement surface conditions is an issue which directly affects the majority of the world today. The complex structure and textural similarities of surface cracks, as well as noise and image illumination variation makes automated detection a challenging task. In this paper, we propose a deep fully convolutional neural network to perform pixel-wise classification of surface cracks on road and pavement images. The network consists of an encoder layer which reduces the input image to a bank of lower level feature maps. This is followed by a corresponding decoder layer which maps the encoded features back to the resolution of the input data using the indices of the encoder pooling layers to perform efficient up-sampling. The network is finished with a classification layer to label individual pixels. Training time is minimal due to the small amount of training/validation data (80 training images and 20 validation images). This is important due to the lack of applicable public data available. Despite this lack of data, we are able to perform image segmentation (pixel-level classification) on a number of publicly available road crack datasets. The network was tested extensively and the results obtained indicate performance in direct competition with that of the current stateof-the-art methods.

Condition and deterioration of public and private infrastructure is an issue that directly affects the majority of the world population. In this paper we propose the application of a Residual Neural Network to automatically detect road and pavement surface cracks. The high amount of variance in the texture of the surface and variation in illumination levels makes the task of automatically detecting defects within public and private infrastructure a difficult task. The system developed utilises a feature pyramid core with an underlying feed-forward ResNet architecture. The output from the feature pyramid then feeds into two sub-networks. One sub-network associates a class with the output from the feature pyramid. The other sub-network regresses the offset from each of the output bounding boxes of the feature pyramid to the corresponding ground truth boxes during training. The network was trained on real world data from an already established dataset. The data used to train and test on is very limited, due to the lack of available road crack datasets in the public domain. Despite the limited amount of data, the proposed method achieves a very positive results with minimal error.

Vitreous attachment was commonly seen at the site of round retinal holes. Vitreous attachment at both edges of the retinal hole in a U-shape configuration was more commonly seen at holes associated with subretinal fluid or RD.