133 research outputs found
DefectNET: multi-class fault detection on highly-imbalanced datasets
As a data-driven method, the performance of deep convolutional neural
networks (CNN) relies heavily on training data. The prediction results of
traditional networks give a bias toward larger classes, which tend to be the
background in the semantic segmentation task. This becomes a major problem for
fault detection, where the targets appear very small on the images and vary in
both types and sizes. In this paper we propose a new network architecture,
DefectNet, that offers multi-class (including but not limited to) defect
detection on highly-imbalanced datasets. DefectNet consists of two parallel
paths, which are a fully convolutional network and a dilated convolutional
network to detect large and small objects respectively. We propose a hybrid
loss maximising the usefulness of a dice loss and a cross entropy loss, and we
also employ the leaky rectified linear unit (ReLU) to deal with rare occurrence
of some targets in training batches. The prediction results show that our
DefectNet outperforms state-of-the-art networks for detecting multi-class
defects with the average accuracy improvement of approximately 10% on a wind
turbine
Automatic Leaf Extraction from Outdoor Images
Automatic plant recognition and disease analysis may be streamlined by an
image of a complete, isolated leaf as an initial input. Segmenting leaves from
natural images is a hard problem. Cluttered and complex backgrounds: often
composed of other leaves are commonplace. Furthermore, their appearance is
highly dependent upon illumination and viewing perspective. In order to address
these issues we propose a methodology which exploits the leaves venous systems
in tandem with other low level features. Background and leaf markers are
created using colour, intensity and texture. Two approaches are investigated:
watershed and graph-cut and results compared. Primary-secondary vein detection
and a protrusion-notch removal are applied to refine the extracted leaf. The
efficacy of our approach is demonstrated against existing work.Comment: 13 pages, India-UK Advanced Technology Centre of Excellence in Next
Generation Networks, Systems and Services (IU-ATC), 201
Image Fusion via Sparse Regularization with Non-Convex Penalties
The L1 norm regularized least squares method is often used for finding sparse
approximate solutions and is widely used in 1-D signal restoration. Basis
pursuit denoising (BPD) performs noise reduction in this way. However, the
shortcoming of using L1 norm regularization is the underestimation of the true
solution. Recently, a class of non-convex penalties have been proposed to
improve this situation. This kind of penalty function is non-convex itself, but
preserves the convexity property of the whole cost function. This approach has
been confirmed to offer good performance in 1-D signal denoising. This paper
demonstrates the aforementioned method to 2-D signals (images) and applies it
to multisensor image fusion. The problem is posed as an inverse one and a
corresponding cost function is judiciously designed to include two data
attachment terms. The whole cost function is proved to be convex upon suitably
choosing the non-convex penalty, so that the cost function minimization can be
tackled by convex optimization approaches, which comprise simple computations.
The performance of the proposed method is benchmarked against a number of
state-of-the-art image fusion techniques and superior performance is
demonstrated both visually and in terms of various assessment measures
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