With the wide application of pressure vessels, their safety has attracted more and more attention. Nowadays, X -ray is often used to detect the weld area of pressure vessels. However, the image content scanned by the existing technology is redundant and the weld area cannot be directly obtained. Therefore, this paper proposes a clipping mapping weld extraction method based on the image characteristics of data sets and Gaussian function algorithm, which has good adaptability and stability, and lays a solid foundation for the subsequent detection and processing of defects in welds.
In order to improve the accuracy and robustness of correlation filtering algorithm in more challenging scenarios, a target tracking algorithm based on adaptive channel sample weights is proposed in this paper. Firstly, channel attention modules are added to sample branch and search branch respectively to weight the features of each branch respectively, which can effectively improve the feature expression ability. Secondly, according to the channel weights of the two branches, we learn to fuse the weight information, interact the features of the two branches, and suppress the background information. Finally, the sample learning weights are reassigned according to the channel weights of the history frame to make more efficient use of the background information. In this paper, the OTB100 data set is used to verify the effectiveness of the improved algorithm. The area under the curve is increased by 1.6%, which proves the effectiveness of the improved algorithm.
In crowded and complex scenes, it is easy to cause problems such as poor human pose estimation and low-key point positioning accuracy. In this paper, a high-resolution human pose estimation algorithm based on position awareness was proposed. The algorithm introduced the coordination attention (CA) in the feature extraction module, which realized the accurate acquisition of the spatial position information of key points, finally improved the human pose. Estimate the detection accuracy of the algorithm. The AP value of the improved algorithm was 76.5%, which was 2.1% higher than the original algorithm, the AP50 was increased by about 3.1%, and the AP75 was increased by about 2.8%. The experimental results showed that the proposed algorithm could effectively improve the detection performance in crowded and complex backgrounds and had higher detection accuracy.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.