Opto-Electronic Engineering, Volume. 49, Issue 3, 210361-1(2022)
An automatic object detection method for microscopic images based on attention mechanism
Figures & Tables(10)
Fig. 1. The basic blocks of ResNet and ResNeXt. (a) Basic block of ResNet; (b) Basic block of ResNeXt
Fig. 3. Microscopic images of the three common pathogenic cells that cause vaginitis. (a) Mildew; (b) Trichomonas; (c) Clue cell
Fig. 4. The performance of the original DETR and the improved DETR on validation dataset
Fig. 5. The comparison of PR curves computed from the original model and the improved model. (a) PR curve of mAP; (b) PR curve of mildew; (c) PR curve of trichomonas; (d) PR curve of clue cell
Fig. 6. The detection results of the three common pathogenic cells. (a) Detection results of mildew; (b) Detection results of trichomonas; (c) Detection results of clue cell
Fig. 7. Comparison of detection results and attention weights visualization map. (a) Original image; (b) Ground truth; (c) Detection results of original DETR; (d) Attention weights visualization of the original DETR; (e) Attention weights visualization of the original DETR on the original image; (f) Detection results of the improved DETR; (g) Attention weights visualization of the improved DETR; (h) Attention weights visualization of the improved DETR on the original image
Table 1. The details of dataset split
View tableView in ArticleTable 1. The details of dataset split
数据集类别 图像张数 标注细胞总数 霉菌个数 滴虫个数 线索细胞个数 训练集 653 1844 1757 63 24 验证集 218 593 562 21 10 测试集 218 620 582 30 8
Table 2. The comparison of mAP and AP results of the original model and the proposed model
View tableView in ArticleTable 2. The comparison of mAP and AP results of the original model and the proposed model
模型 mAP/% 霉菌AP/% 滴虫AP/% 线索细胞AP/% 原始DETR 87.5 87.0 85.9 89.7 本文改进模型 96.3 93.0 95.8 100.0
Table 3. The comparison of precision and recall results of the original DETR and the proposed improved DETR
View tableView in ArticleTable 3. The comparison of precision and recall results of the original DETR and the proposed improved DETR
评价指标 模型 霉菌 滴虫 线索细胞 查准率(precision)/% 原始DETR 85.3 90.0 100 本文改进模型 88.0 100 100 召回率(recall)/% 原始DETR 85.0 90.0 75.0 本文改进模型 91.0 93.0 100
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Ruqian Hao, Xiangzhou Wang, Jing Zhang, Juanxiu Liu, Xiaohui Du, Lin Liu. An automatic object detection method for microscopic images based on attention mechanism[J]. Opto-Electronic Engineering, 2022, 49(3): 210361-1
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Received: Nov. 13, 2021
Accepted: --
Published Online: Apr. 24, 2022
The Author Email: Liu Juanxiu (juanxiul@qq.com)
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