中图分类号：TP391.41；U418.6 文献标志码：A DOI: 10.16157/j.issn.0258-7998.245560
中文引用格式： 万成凯，李居朋. 基于改进CenterNet的发票检测算法[J]. 电子技术应用，2025，51(6)：71-78.
英文引用格式： Wan Chengkai，Li Jupeng. Detection algorithm for invoice based on improved CenterNet[J]. Application of Electronic Technique，2025，51(6)：71-78.

Detection algorithm for invoice based on improved CenterNet

Abstract： In order to improve the accuracy and efficiency of invoice detection, a CenterNet based invoice detection algorithm is proposed. Firstly, the algorithm model adopts a backbone network similar to CSPDarkNet, introducing Triplet Attention into the CSP structure to form a TA-CSP structure, and introducing ASPP at the end of the backbone network to improve the receptive field range of the network, enabling the model to better understand the contextual information of the image; Secondly, in the Neck part of the network, CBAM is used to guide the fusion of high-level and low-level features, and the semantic information in high-level feature maps is used to supervise low-level feature maps to suppress background noise in low-level feature maps; Thirdly, in the Head section of the network, the algorithm adds four channels of feature map outputs based on the CenterNet network, achieving invoice orientation prediction while detecting invoices; Finally, an orientation loss term is added to the loss function to optimize the orientation of invoices. The experimental results on the test dataset show that the mAP of the proposed algorithm in this paper is superior to CenterNet and YOLOv5s algorithms reaching 84.3%, effectively improving the accuracy and robustness of invoice detection.

Key words : CenterNet；YOLO；object detection；CBAM；ASPP；Triplet Attention