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Insufficient illumination and illumination variation in image sequences make it challenging for algorithms to obtain clear outlines for objects in motion. This study proposes a high-performance adaptive dual fractional-order variational optical flow model which could be used to resolve these issues. The proposed method revitalises the original dual fractional-order optical flow model and adopts a fractional differential mask in both the data and smoothness terms of the traditional Horn-Schunck model. The main innovation of this work is to fit a flow field regional to a variety of fractional-order differential masks. The domain of each region is determined adaptively. The order and size of the fractional-order differential masks for each region are adjusted by image signal to noise ratio while the shape of the fractional-order differential mask is regulated to prevent interference from surrounding regions. Adjusting the fractional-order differential mask adaptively enables the proposed method to accurately segment motion objects in poor and variable illumination regions as well. The experimental results show that our algorithm outperforms the current state-of-the-art algorithms on low-light real scene videos and also achieves competitive results on the Middlebury, KITTI and MPI Sintel public benchmarks.

This paper presents an automation approach towards the detection and classification of cracks on bridge surfaces using a robot platform. The approach is designed to exploit the physical features of cracks and is therefore capable of overcoming the challenges that traditional crack detection approaches are faced with. The approach adopts the Beamlet and Wavelet Transforms in the realization of a robust crack segmentation scheme. The Radon transform is coupled with the Projection Variance towards the extraction of crack features which facilitates a high specificity even in the presence of noise and texture irregularities. Finally, in order to render all this information useful and applicable towards the maintenance of bridges, a classification scheme is proposed which classifies cracks into non-crack, simple crack and complex crack categories. The classification scheme is realized through an AdaBoosted RVM implementation that achieves a high classification accuracy and generalization. This detection and classification system is deployed on the six-legged robot platform designed to operate semi-autonomously on bridges. The performance of this scheme is verified through comparison experiments with state-of-the-art and the experimental results indicate that the proposed scheme achieves effective results while outperforming some of the state-of-the art in terms of accuracy and classifier training time. 