Object recognition based on improved YOLOv5

At presen, object recognition task is troubled by its huge kinds of objects. In this paper, the SIoU loss function and YOLOv5 deep learning convolutional neural network are innovatively used to improve the training efficiency and recognition accuracy. Unlike the traditional bounding box regression loss function (e.g. Giou, Diou[1] , CIoU) , which only focuses on the distance between the prediction box and the ground true box, the size of the overlap area, and one or more of the aspect ratios, and sets the impact factor on this basis, the SIoU loss function also introduces Angle cost to fit the best regression direction, which makes the direction of bounding box regression more reasonable and improves the regression efficiency[1].In this paper, the defects of traditional loss function and the calculation method of SIoU loss function are introduced, and the performance between SIoU and CIoU is compared.