多尺度特征在YOLO算法中的应用研究

Application research of multi-scale features in YOLO slgorithm

针对YOLO算法在下采样过程中丢失了部分大尺寸特征图的有效信息,从而导致在检测任务中因目标定位不够精准而影响模型整体检测精度的问题。提出利用多尺度特征融合的方法来解决YOLO定位不精准的问题,首先,对YOLO算法的网络模型进行修改,利用YOLO网络模型中不同尺寸特征图具有不同特征属性的特点,融合不同尺寸特征图来提高检测网络对目标的定位精度;其次在预训练模型的基础上对修改后的网络模型进行重新训练;最后在计算机中对训练好的模型进行检测试验。实验结果表明,基于多尺度特征的YOLO目标检测算法在精确率上相对于YOLO目标检测算法提高了3.02%,mAP提高了1.53%。

YOLO algorithm loses part of the effective information of large-size feature maps in the downsampling process,which leads to the problem of insufficient target location in the detection task,which affects the overall detection accuracy of the model.This paper proposes the use of multi-scale feature fusion to solve the problem of inaccurate location of YOLO;First,modify the network model of YOLO algorithm,use different size feature maps in the YOLO network model with different feature attributes,and merge different size feature maps to improve the location accuracy of the detection network to the target;second,based on the pre-training model Re-train the modified network model on the last;finally,test the trained model in the computer.Experimental results show that the YOLO target detection algorithm based on multi-scale features improves the Accuracy rate by 3.02%and improves mAP by 1.53%compared with YOLO target detection algorithm.