基于改进的YOLOX血细胞检测算法研究

Research on improved blood cell detection algorithm based on YOLOX

血细胞计数是一种常见的临床检验方法。针对血液显微镜图像中的血细胞种类不均匀、密集且相互遮挡导致现有血细胞检测方法准确率不高,提出了一种改进的YOLOX血细胞检测算法。该算法首先在损失函数中引入Focal loss以改善单阶段目标检测算法正负样本的不平衡和细胞种类不均匀的问题;接着在残差模块中引入混合注意力机制,减少了血细胞相互遮挡造成的漏检、错检的概率;然后在特征融合尾部引入自适应空间特征融合模块以提高特征表达能力;最后在残差模块中引入逆深度可分离卷积模块在减少模型参数的同时还略微提高检测精度。提出的算法在BCCD血细胞数据集进行了测试,改进后的YOLOX算法在血细胞数据集上的检测精度达到了92.5%,相比YOLOX算法提升了2.4%,且减少了8%的模型参数量;该算法在COCO2017通用数据集上的检测精度达到了41.7%,相对于原始YOLOX算法提升了1.2%。

Blood count is a common clinical test.Aiming at the low accuracy of the existing blood cell detection methods due to the uneven,dense and mutual occlusion of blood cells in blood microscope images,an improved YOLOX blood cell detection algorithm is proposed.The algorithm firstly addes Focal loss into the loss function to improve the imbalance of positive and negative samples and uneven cell types in the single-stage target detection algorithm.Then,a mixed attention mechanism is added into the residual module,which reduces the probability of missed detection and false detection caused by the mutual occlusion of blood cells.Then an adaptive spatial feature fusion module is added at the end of the feature fusion to improve the feature expression ability.Finally,an inverse depth separable convolution module is added into the residual module to reduce model parameters and slightly improve detection precision.The proposed algorithm is tested on the BCCD blood cell data set,and the detection accuracy of the improved YOLOX algorithm on the blood cell data set is 92.5%,which is 2.4%higher than that of the YOLOX algorithm,and the model parameters are reduced by 8%;The detection accuracy of the algorithm on the COCO2017 general data set is 41.7%,which is 1.2%higher than that of the original YOLOX algorithm.