Abstract:
“Edge Detection Using Deep Learning”, is a project that is based on research which is
key objective to improve a novel detection of edge technique which introduces a pair key
flaws of long-term vision recognition by computer issue. We have implemented a deep
learning-based edge detection technique termed holistically-nested edge detection in this
research. This edge detection introduces two important issues: The first is nested multiscale feature learning, which is encouraged by deep convolutional neural networks for
image to image prediction. The second is holistic image training and prediction. An imageto-image estimate is achieved using a HED technique. This method is suggested for
determining object boundaries. A deep learning method is used to discover edges that are
holistically nested. This model completely combines deeply supervised and fully
convolutional neural networks. On the BSD500 dataset and on the NYU Depth dataset
where ODS F-score of 0.782 and 0.746 respectively. We do it at a significantly faster pace
of 0.4s per image than some recent CNN-based edge detection techniques.