A Uyghur text detection method in natural scenes based on improved CenterNet

Aiming at the problems of difficult Uyghur feature extraction, easy missed detection of targets and low detection efficiency, a Uyghur text detection method based on CenterNet is proposed. Firstly, the lightweight EfficientNetV2-S is used as the backbone network to reduce the network parameters and improve the ability of the network to extract features. Then, the SE module in the backbone network EfficientNetV2-S is replaced by the ECA module to improve the network operation speed of the network. In addition, according to the characteristics of the diversity of Uyghur target size, the deconvolution module in CenterNet is removed, and the pyramid network of features is introduced to enhance the adaptability of the model to Uyghur with different target sizes and improve the generalization ability of the model. The experimental results on the Uyghur dataset show that the average precision of the improved CenterNet for Uyghur detection in natural scenes reaches 94.2 %, which is 5.9 % higher than that of the original CenterNet, and the detection effect on Uyghur is better.