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    • 摘要: 显微图像普遍具有背景复杂、细胞多重叠的特点,传统的图像处理方法由于其技术局限性,无法实时准确地完成识别任务。针对上述问题,本文提出一种采用注意力机制的显微图像智能检测方法,该方法对目标检测模型DETR进行改进,利用分组卷积机制对输入特征进行降维并分别训练不同卷积核实现特征提取,从而提高模型对于显微图像中目标物体的注意力,并增加了检测准确率。本文以阴道炎致病微生物检测为应用场景,实验结果表明,改进的模型平均查准率均值mAP为96.3%,比原模型提高约10%。同时,本方法在细胞重叠、粘连以及背景杂质繁多等场景下,仍具有高鲁棒性和强检测能力。每张显微图像检测耗时约为88.8 ms,可以满足实时检测的需求。因此,本文提出的模型可实时准确地对常见的阴道炎致病微生物进行实时高效自动检测,可满足显微镜检临床使用的实际需求。

       

      Abstract: The microscopic image has the characteristics of complex background and overlapping cells. Due to the technical limitations, traditional image processing methods cannot accurately complete the real-time recognition task. To address the above-mentioned problems, we propose an automatic detection method for microscopic images using attention mechanism. This method improves the original DETR architecture by introducing a split-transform-merge mechanism, which reduces the dimensionality of input features and trains multiple groups of convolution kernels for feature extraction, thereby effectively improving the model's feature extraction ability for the targets and increasing the accuracy of model detection rate. The experimental results show that the mAP of the improved model was 96.3%, which is 10% higher than that of the original model DETR. Meanwhile, the proposed method has superior detection capabilities for scenarios such as cell overlap, adhesion, and complex background. Moreover, the detection time for each leucorrhea image was about 88.8 ms, which can satisfy the requirement of real-time microscopy examination.