Integrated Method for Road Extraction: Deep Convolutional Neural Network Based on Shape Features and Images

Abstract

As a significant application in RS images, road detection is still a challenging task due to the presence of complex surroundings and multiple false objects. To achieve a satisfying result, a road detection method based on residual learning and saliency sampling is developed in this paper. First, a multistrapdown module is designed with double residual learning blocks that have low computational complexity and time consumption. Second, to improve the classification accuracy and learning ability of the method, a saliency sampling set is established by fusing brightness, orientation and texture maps. The sampling set is imported and merged into the model through the pooling layer. Finally, the cross entropy loss function is carried out in a Softmax classifier. Extensive experiments show that our proposed integrated method is much better than the state-of-the art methods in detection accuracy.

A Deep Neural Network-Based Model for Quantitative Evaluation of the Effects of Swimming Training

“…Up to now, the theoretical research about deep BP neural networks is still in its initial stage except for the corresponding explanation from the bionic perspective. Still, it has achieved excellent results in many applications to perform deep learning of functions [23,24]. At the same time, its use in image recognition is relatively early and more mature; Lin et al explains referring to a deep neural network with a convolutional structure inspired by a biological vision model called a convolutional neural network [25].…”

An Image Classification Method of Unbalanced Ship Coating Defects Based on DCCVAE-ACWGAN-GP

“…Conventional coating defect detection is the inspection and judging of coating quality and the recording of the type and level of defects the staff produces within a specific time after the painting is completed. This method relies on domain experts or field technicians to identify the underlying visual features of defects, such as color [9][10][11][12], shape [13][14][15][16], and texture [17][18][19][20] through professional knowledge and practical experience, which not only increases the work intensity and work pressure of personnel but also greatly increases time cost, making it difficult to ensure the accuracy of coating defect identification [21] and the efficiency of coating operations [22]. With proposals based in ship manufacturing theory and the continuous popularization of artificial intelligence in the ship painting industry, people have begun to gradually apply intelligent technology to the understanding of ship painting defects, but there are fewer reports on the application of image-based ship coating defects recognition.…”