1 research outputs found
Deep hashing network for material defect image classification
Common nonâdestructive material testing technology has some wellâknown problems such as slow detection, low detection accuracy, and low level of information obtained. To solve these problems, this study applied recent advances in convolution neural networks to propose an effective deep learning network using casting datasets. The approach achieves nonâdestructive material testing with automatic, intelligent detection technology. For most existing deep learning networks, an image is eventually transformed into a multidimensional visual feature vector for comparison and classification. However, such vectors may not optimally improve detection precision and speed, and can lead to significant storage problems. A deep hashing network is proposed in which images are mapped into compact binary codes. There are three key components: (i) a subânetwork with multiple convolutionâpooling layers to capture image representations; (ii) a hashing layer to generate compact binary hash codes; (iii) an encoder module to divide the image feature vector from the output of the subânetwork above into multiple branches, each encoded into one hash bit. Extensive experiments using a casting dataset show promising performance compared with the stateâofâtheâart approach