It is important to accurately classify the defects in hot rolled steel strip since the detection of defects in hot rolled steel strip is closely related to the quality of the final product. The lack of actual hot-rolled strip defect data sets currently limits further research on the classification of hot-rolled strip defects to some extent. In real production, the convolutional neural network (CNN)-based algorithm has some difficulties, for example, the algorithm is not particularly accurate in classifying some uncommon defects. Therefore, further research is needed on how to apply deep learning to the actual detection of defects on the surface of hot rolled steel strip. In this paper, we proposed a hot rolled steel strip defect dataset called Xsteel surface defect dataset (X-SDD) which contains seven typical types of hot rolled strip defects with a total of 1360 defect images. Compared with the six defect types of the commonly used NEU surface defect database (NEU-CLS), our proposed X-SDD contains more types. Then, we adopt the newly proposed RepVGG algorithm and combine it with the spatial attention (SA) mechanism to verify the effect on the X-SDD. Finally, we apply multiple algorithms to test on our proposed X-SDD to provide the corresponding benchmarks. The test results show that our algorithm achieves an accuracy of 95.10% on the testset, which exceeds other comparable algorithms by a large margin. Meanwhile, our algorithm achieves the best results in Macro-Precision, Macro-Recall and Macro-F1-score metrics.

In recent years, benefiting from deep convolutional neural networks (DCNNs), face parsing has developed rapidly. However, it still has the following problems: (1) Existing state-of-the-art frameworks usually do not satisfy real-time while pursuing performance; (2) similar appearances cause incorrect pixel label assignments, especially in the boundary; (3) to promote multi-scale prediction, deep features and shallow features are used for fusion without considering the semantic gap between them. To overcome these drawbacks, we propose an effective and efficient hierarchical aggregation network called EHANet for fast and accurate face parsing. More specifically, we first propose a stage contextual attention mechanism (SCAM), which uses higher-level contextual information to re-encode the channel according to its importance. Secondly, a semantic gap compensation block (SGCB) is presented to ensure the effective aggregation of hierarchical information. Thirdly, the advantages of weighted boundary-aware loss effectively make up for the ambiguity of boundary semantics. Without any bells and whistles, combined with a lightweight backbone, we achieve outstanding results on both CelebAMask-HQ (78.19% mIoU) and Helen datasets (90.7% F1-score). Furthermore, our model can achieve 55 FPS on a single GTX 1080Ti card with 640 × 640 input and further reach over 300 FPS with a resolution of 256 × 256, which is suitable for real-world applications.

Diabetic retinopathy (DR) is a common fundus disease that leads to irreversible blindness, which plagues the working-age population. Automatic medical imaging diagnosis provides a non-invasive method to assist ophthalmologists in timely screening of suspected DR cases, which prevents its further deterioration. However, the state-of-the-art deep-learning-based methods generally have a large amount of model parameters, which makes large-scale clinical deployment a time-consuming task. Moreover, the severity of DR is associated with lesions, and it is difficult for the model to focus on these regions. In this paper, we propose a novel deep-learning technique for grading DR with only image-level supervision. Specifically, we first customize the model with the help of self-knowledge distillation to achieve a trade-off between model performance and time complexity. Secondly, CAM-Attention is used to allow the network to focus on discriminative zone, e.g., microaneurysms, soft/hard exudates, etc.. Considering that directly attaching a classifier after the Side branch will disrupt the hierarchical nature of convolutional neural networks, a Mimicking Module is employed that allows the Side branch to actively mimic the main branch structure. Extensive experiments are conducted on two benchmark datasets, with an AUC of 0.965 and an accuracy of 92.9% for the Messidor dataset and 67.96% accuracy achieved for the challenging IDRID dataset, which demonstrates the superior performance of our proposed method.

In this article, a low-leakage-current and high-breakdown-voltage normally OFF p-GaN gate high-electron-mobility transistor (HEMT) on a Si substrate with low-damage NH $_{\text{3}}$ plasma pretreatment was investigated. NH $_{\text{3}}$ plasma pretreatment was performed in a plasma-enhanced chemical vapor deposition (PECVD) system before PECVD-SiN $_{\textit{x}}$ deposition, which effectively removed surficial oxides and other impurities, suppressed the interface traps between those of SiN $_{\textit{x}}$ and (Al)GaN, and reduced the overall drain leakage current of devices in the OFF-state. In comparison to the device without pretreatment, the normally OFF p-GaN gate HEMTs pretreated with NH $_{\text{3}}$ plasma exhibited a high ON/OFF-current ratio of 10 $^{\text{9}}$ , a low reverse gate leakage current of 2.86 $\times$ 10 $^{-\text{8}}$ mA/mm, a high gate breakdown voltage of 9.1 V, and a high breakdown voltage of 690 V at 1 $\mu $ A/mm. Furthermore, NH $_{\text{3}}$ plasma pretreatment effectively suppressed the threshold voltage shift induced by the charge/discharge effect in p-GaN. It was found that NH $_{\text{3}}$ plasma pretreatment is effective in improving device performance, such as the leakage current, breakdown voltage, and threshold voltage stability.

Existing methods for detecting surface defects in strip steel are mainly aimed at the case of low-resolution images, and little research has been done in the case of high-resolution images with a small percentage of valid targets. In response to the practical problems in the hot-rolled flattening stage, we propose a hot-rolled strip surface defect dataset called XLData-CLS, which contains a total of 5000 defective samples and 10,000 normal samples with an image resolution of 1344 × 672 pixels. Based on this dataset, this paper improves the ResNet152 model by adding a feature extraction layer at the front of the model so that it can extract information more effectively for high-resolution images. Experimental results on the XLData-CLS dataset show that the proposed method achieves 99.00% classification accuracy on the test set, which is higher than other comparable deep learning models. Validation results on another datasets also demonstrate the accuracy of our method for high-resolution defective images.