Abstract: YOLOv5 model is currently one of the best models for object detection.To solve the problem of different lengths of text lines,the inclination of text,light and shadow in natural scenes,etc.the R-YOLOv5(Rotated-YOLOv5) text detection model is proposed,which improves the YOLOv5 model to deal with the weakness in text detection.Firstly,the text segmentation model based on affine algorithm is incorporated.According to the length of the string and the shape of the text area,the text area of the picture is cut into multiple single-character blocks in equal proportions to solve the problem of poor effect of YOLOv5 model caused by the text objects without closed contour lines.Then,using the rotated convolutional neural network layer,rotated max-pooling layer and improved anchor box,we propose a rotated intersection over union(RIoU) loss function that strengthens angle learning to achieve the extraction of rotation and tilt features.The original model and the improved model are tested on ICDAR2019-LSVT.Experimental results show that the detection effect of R-YOLOv5 are significantly improved.However,due to the deepening of model layers,the training efficiency and detection efficiency are slightly reduced compared with the original mo-del.Compared with other models,due to the advantages of YOLOv5,the detection effect and efficiency of R-YOLOv5 are much better than that of other models.

Key words: Computer vision, Object detection, Text detection, Convolutional neural network, Rotation tilt, Loss function, YOLO