Improved YOLO Framework Blood Cell Detection Algorithm.

In order to solve the problems of low accuracy, wrong detection and missing detection of traditional target detection algorithms in blood cell detection task, a target detection algorithm YOLO-Att based on improved YOLO framework is proposed. Based on the YOLO framework, a multi-scale residual enhancement module is added to the backbone network to improve the utilization rate of feature information by combining with the feature level of low-level informationrich network. An attentional gating structure embedding model is designed to obtain more high quality information of main features. At the same time, Focal loss is used to replace the cross entropy in the original loss function to improve the weight of positive and negative samples and accelerate the convergence rate of the model. Finally, K -means++ clustering algorithm is used to optimize the anchor frame of the target to further improve the detection accuracy. Compared with the existing target detection algorithms such as Faster-RCNN, SSD and YOLOv4, YOLO-Att increased mAP to 66.32% in the BCCD detection task of the universal blood cell data set, and the detection rate reaches 85.4 ms, which is more in line with the real-time detection task of blood cell. [ABSTRACT FROM AUTHOR]