Intelligent Detection of Steel Defects Based on Improved Split Attention Networks

The intelligent monitoring and diagnosis of steel defects plays an important role in improving steel quality, production efficiency, and associated smart manufacturing. The application of the bio-inspired algorithms to mechanical engineering problems is of great significance. The split attention network is an improvement of the residual network, and it is an improvement of the visual attention mechanism in the bionic algorithm. In this paper, based on the feature pyramid network and split attention network, the network is improved and optimised in terms of data enhancement, multi-scale feature fusion and network structure optimisation. The DF-ResNeSt50 network model is proposed, which introduces a simple modularized split attention block, which can improve the attention mechanism of cross-feature graph groups. Finally, experimental validation proves that the proposed network model has good performance and application prospects in the intelligent detection of steel defects.

Developing a seq2seq neural network using visual attention to transform mathematical expressions from images to LaTeX.

The paper presents the methods of development and the results of research on the effectiveness of the seq2seq neural network architecture using Visual Attention mechanism to solve the im2latex problem. The essence of the task is to create a neural network capable of converting an image with mathematical expressions into a similar expression in the LaTeX markup language. This problem belongs to the Image Captioning type: the neural network scans the image and, based on the extracted features, generates a description in natural language. The proposed solution uses the seq2seq architecture, which contains the Encoder and Decoder mechanisms, as well as Bahdanau Attention. A series of experiments was conducted on training and measuring the effectiveness of several neural network models.

RNIC-A Retrospect Network for image captioning

As cross-domain research combining computer vision and natural language processing, the current image captioning research mainly considers how to improve the visual features, less attention has been paid to utilizing the inherent properties of language to boost captioning performance. Facing this challenge, we proposed a textual attention mechanism, which can obtain semantic relevance between words by scanning all generated words. The Retrospect Network for image captioning(RNIC) proposed in this paper aims to improve input and prediction process by using textual attention. Concretely, the textual attention mechanism is applied to the model simultaneously with the visual attention mechanism to provide the input of the model with the maximum information required for generating captions. In this way, our model can learn to collaboratively attend on both visual and textual features. Moreover, the semantic relevance between words obtained by retrospect is used as the basis for prediction, so that the decoder can simulate the human language system and better make predictions based on the already generated contents. We evaluate the effectiveness of our model on the COCO image captioning datasets and achieve superior performance overthe previous methods.extraction function to extract the hidden unit information of multiple time steps for prediction, to solve the problem of insufficient LSTM prediction information. Experiments have shown that both model significantly improved the various evaluation indicators in the AI CHALLENGER test set.

Ship Object Detection of Remote Sensing Image Based on Visual Attention

At present, reliable and precise ship detection in high-resolution optical remote sensing images affected by wave clutter, thin clouds, and islands under complex sea conditions is still challenging. At the same time, object detection algorithms in satellite remote sensing images are challenged by color, aspect ratio, complex background, and angle variability. Even the results obtained based on the latest convolutional neural network (CNN) method are not satisfactory. In order to obtain more accurate ship detection results, this paper proposes a remote sensing image ship object detection method based on a brainlike visual attention mechanism. We refer to the robust expression mode of the human brain, design a vector field filter with active rotation capability, and explicitly encode the direction information of the remote sensing object in the neural network. The progressive enhancement learning model guided by the visual attention mechanism is used to dynamically solve the problem, and the object can be discovered and detected through time–space information. To verify the effectiveness of the proposed method, a remote sensing ship object detection data set is established, and the proposed method is compared with other state-of-the-art methods on the established data set. Experiments show that the object detection accuracy of this method and the ability to capture image details have been improved. Compared with other models, the average intersection rate of the joint is 80.12%, which shows a clear advantage. The proposed method is fast enough to meet the needs of ship detection in remote sensing images.

Leveraging colour-based pseudo-labels to supervise saliency detection in hyperspectral image datasets

Saliency detection mimics the natural visual attention mechanism that identifies an imagery region to be salient when it attracts visual attention more than the background. This image analysis task covers many important applications in several fields such as military science, ocean research, resources exploration, disaster and land-use monitoring tasks. Despite hundreds of models have been proposed for saliency detection in colour images, there is still a large room for improving saliency detection performances in hyperspectral imaging analysis. In the present study, an ensemble learning methodology for saliency detection in hyperspectral imagery datasets is presented. It enhances saliency assignments yielded through a robust colour-based technique with new saliency information extracted by taking advantage of the abundance of spectral information on multiple hyperspectral images. The experiments performed with the proposed methodology provide encouraging results, also compared to several competitors.