A Fast Horizontal Text Detection Method Based on the Improved YOLOv5
The text detection methods based on deep learning has strong feature learning ability and generalization ability,but the inference speed is usually slow.To solve this problem,a fast horizontal text detection method T-YOLOv5 based on improved YOLOv5 is proposed.By em-bedding an improved CAM(channel attention module)in SPPF(spatial pyramid pooling-fast)module,the feature extraction ability of the network is improved,and the shape loss is added to the CIoU(complete IoU)loss to improve the convergence speed of the loss function.The F value of the proposed method reaches 86.5 on the public dataset ICDAR2013,and the inference speed reaches 112 FPS.Experimental results show that the proposed method T-YOLOv5 is competitive with the existing text detection methods based on deep learning in terms of detection results and inference speed.
万方数据
维普
