End-to-end recognition of slab identification numbers using a deep convolutional neural network

Two-stage hybrid algorithm for recognition of industrial slab numbers with data quality improvement

As the unique recognition of each slab, the accurate recognition of slab number is especially critical for the hot rolling production process. However, the collected data are often of low quality due to poor production environment conditions, making traditional deep learning algorithms face more significant challenges in slab numbers recognition. In this paper, a two-stage hybrid algorithm based on convolutional neural network and Transformer is proposed to identify industrial slab numbers. In the first stage, an improved CycleGAN (HybridCy) is developed to enhance the quality of real-world unpaired data. In the second stage, a multi-scale hybrid vision transformer model (MSHy-Vit) is proposed to identify slab numbers of the improved data output of stage one. The experimental results on industrial slab data show that HybridCy exhibits stable and efficient performance. Even for low-quality data with severe geometric distortion, HybridCy can accomplish quality improvement, which can help to improve recognition accuracy. In addition, the MSHy-Vit achieves superior accuracy in the recognition of slab numbers in comparison to existing methods in the literature.

Detection of rice plant diseases based on deep transfer learning

BACKGROUND

As the primary food for nearly half of the world's population, rice is cultivated almost all over the world, especially in Asian countries. However, the farmers and planting experts have been facing many persistent agricultural challenges for centuries, such as different diseases of rice. The severe rice diseases may lead to no harvest of grains; therefore, a fast, automatic, less expensive and accurate method to detect rice diseases is highly desired in the field of agricultural information.

RESULTS

In this article, we study the deep learning approach for solving the task since it has shown outstanding performance in image processing and classification problem. Combining the advantages of both, the DenseNet pre-trained on ImageNet and Inception module were selected to be used in the network, and this approach presents a superior performance with respect to other state-of-the-art methods. It achieves an average predicting accuracy of no less than 94.07% in the public dataset. Even when multiple diseases were considered, the average accuracy reaches 98.63% for the class prediction of rice disease images.

CONCLUSIONS

The experimental results prove the validity of the proposed approach, and it is accomplished efficiently for rice disease detection. © 2020 Society of Chemical Industry.