Detecting product surface defects is an important issue in industrial scenarios. In the actual scene, the shooting angle and the distance between the industrial camera and the shooting object often vary, which results in a large variation in the scale and angle. In addition, high-speed cameras are prone to motion blur, which further deteriorates the defect detection results. In order to solve the above problems, this study proposes a surface defect detection model for industrial products based on attention enhancement. The network takes advantage of the lower-level and higher-resolution feature map from the backbone to improve Path Aggregation Network (PANet) in object detection. This study makes full use of multihead self-attention (MHSA), an independent attention block for enhancing the backbone network, which has made considerable progress for practical application in industry and further improvement of the surface defect detection. Moreover, some tricks have been adopted that can improve the detection performance, such as data augmentation, grayscale filling, and channel conversion of input images. Experiments in this study on internal datasets and four public datasets demonstrate that our model has achieved good performance in industrial scenarios. On the internal dataset, the mAP@.5 result of our model is 98.52%. In the RSDDs dataset, the model in this study achieves 86.74%. In the BSData dataset, the model reaches 82.00%. Meanwhile, it achieves 81.09% and 74.67% on the NRSD-MN and NEU-DET datasets, respectively. This study has demonstrated the effectiveness and certain generalization ability of the model from internal datasets and public datasets.
Our study aims to understand the impact of open government data (OGD) policy on firm performance and the moderating role of firm characteristics. The difference‐in‐difference model is used to analyze 10‐year panel data from 477 Chinese listed firms. The findings indicate that (a) OGD policy positively affects firm performance; (b) firm size positively moderates the relationship between OGD policy and firm performance; (c) R&D intensity nonlinearly moderates the relationship between OGD policy and firm performance; and (d) OGD policy significantly affects state‐owned firms. This paper provides suggestions for improving OGD policy and OGD market construction.
Formula recognition is widely used in document intelligent processing, which can significantly shorten the time for mathematical formula input, but the accuracy of traditional methods could be higher. In order to solve the complexity of formula input, an end-to-end encoder-decoder framework with an attention mechanism is proposed that converts formulas in pictures into LaTeX sequences. The Vision Transformer (VIT) is employed as the encoder to convert the original input picture into a set of semantic vectors. Due to the two-dimensional nature of mathematical formula, in order to accurately capture the formula characters’ relative position and spatial characteristics, positional embedding is introduced to ensure the uniqueness of the character position. The decoder adopts the attention-based Transformer, in which the input vector is translated into the target LaTeX character. The model adopts joint codec training and Cross-Entropy as a loss function, which is evaluated on the im2latex-100k dataset and CROHME 2014. The experiment shows that BLEU reaches 92.11, MED is 0.90, and Exact Match(EM) is 0.62 on the im2latex-100k dataset. This paper’s contribution is to introduce machine translation to formula recognition and realize the end-to-end transformation from the trajectory point sequence of formula to latex sequence, providing a new idea of formula recognition based on deep learning.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.