Journal of Shandong University(Engineering Science) ›› 2026, Vol. 56 ›› Issue (4): 84-93.doi: 10.6040/j.issn.1672-3961.0.2025.114

• Machine Learning & Data Mining • Previous Articles    

CBAM-U-Net: segmentation of potato pollen images based on U-Net and self-attention mechanism

Shen Yajie1, Xia Lu1, Li Jie1, Tang Mingjing1,2*   

  1. Shen Yajie1, Xia Lu1, Li Jie1, Tang Mingjing1, 2*(1. School of Information Science and Technology, Yunnan Normal University, Kunming 650500, Yunnan, China;
    2. Yunnan Provincial Key Laboratory of Potato Biology, Yunnan Normal University, Kunming 650500, Yunnan, China
  • Published:2026-08-12

Abstract: To address the low efficiency, subjectivity, and poor adaptability to complex backgrounds of traditional manual observation and threshold-based methods in potato pollen microscopy image segmentation and counting, a pollen segmentation method integrating the convolutional block attention module(CBAM)and U-Net(CBAM-U-Net)was proposed. Built on the U-Net framework, the CBAM was introduced to improve the extraction of key features and edge details in pollen regions, while median filtering and histogram equalization were applied for image preprocessing. Experiments on 44 high-resolution potato pollen images and more than 5 000 microscopic images showed that the proposed method achieved accurate pollen segmentation, improved segmentation efficiency by about five times over traditional methods, and demonstrated good robustness and generalization ability on large-scale datasets. The proposed method could improve the automation and accuracy of potato pollen microscopy image analysis and provide technical support for microscopic image analysis in crop breeding, cell biology, and precision agriculture.

Key words: U-Net, CBAM, potato pollen, high throughput segmentation, threshold segmentation

CLC Number: 

  • TP391.9
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