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Asymmetric convolutional multi-level attention network for micro-lens segmentation.
- Source :
-
Engineering Applications of Artificial Intelligence . Jul2024:Part A, Vol. 133, pN.PAG-N.PAG. 1p. - Publication Year :
- 2024
-
Abstract
- Tiny target recognition in automation is currently a hot research task that usually suffers from typical issues such as complex background, dim target, and slow detection speed. In the current study, a data-driven method is proposed to realize the posture recognition of micro-lens during optical device coupling to achieve accurate clamping of the gripper. First, we establish a pixel-by-pixel labeled optical micro-lens dataset named single-frame micro-lens target (SFMT), which provides data support for the subsequently proposed convolutional neural network. Subsequently, an asymmetric convolutional multi-level attention network (ACMANet) is proposed to realize accurate segmentation detection of micro-lenses by employing an embedded multi-scale asymmetric convolutional module (MACM) and a multi-level interactive attention module (MIAM). MACM achieves not only a reduction in computational complexity but also enhanced robustness for rotated image recognition through multi-scale asymmetric convolutional kernels. Furthermore, MIAM improves the accuracy of image segmentation by connecting the down-sampling and up-sampling stages and realizing the fusion of pixel position details and key channel features. Extensive experimental results based on our self-constructed image acquisition system demonstrate that the values of normalized intersection over union and dice are successively 91.41% and 95.50%, and the processing speed is 3.3 s/100 images, which shows the advance of ACMANet. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 09521976
- Volume :
- 133
- Database :
- Academic Search Index
- Journal :
- Engineering Applications of Artificial Intelligence
- Publication Type :
- Academic Journal
- Accession number :
- 177605625
- Full Text :
- https://doi.org/10.1016/j.engappai.2024.108355