A large number of photos are taken for each athlete during a marathon competition, therefore, how to classify photos of specific athletes accurately and effectively has become the focus of attention. In this paper, we propose a compound deep neural network for marathon athletes number recognition to make classification more efficient and accurate. The proposed model is divided into three modules: image preprocessing module, text detection module, and text recognition module. Firstly, in the preprocessing module, we make use of the You Only Look Once version 3, and set the detection threshold and similarity threshold to reduce unnecessary detection. Secondly, we combine the efficient text detector Connectionist Text Proposal Network and the excellent text recognition general framework Convolutional Recurrent Neural Network (CRNN) to recognize the athletes number plates. Besides, to improve the accuracy of detection, we use transfer learning to fine-tune the CRNN. Finally, we design an effective tree filtering algorithm to avoid the interference caused by the text detection module. It can filter out invalid results, thereby improving the accuracy of the model. Our model is capable of performing classification on photos of marathon athletes with high precision. The model is feasible and effective, as indicated by the experiment results.

中文翻译：

---

基于复合深度神经网络的马拉松运动员号码识别模型

马拉松比赛中，每个运动员的照片都会有大量的拍摄，因此，如何准确有效地对特定运动员的照片进行分类，成为人们关注的焦点。在本文中，我们提出了一种用于马拉松运动员号码识别的复合深度神经网络，使分类更加高效和准确。所提出的模型分为三个模块：图像预处理模块、文本检测模块和文本识别模块。首先，在预处理模块中，我们利用了 You Only Look Once 版本 3，并设置了检测阈值和相似性阈值，以减少不必要的检测。第二，我们结合了高效的文本检测器 Connectionist Text Proposal Network 和优秀的文本识别通用框架卷积循环神经网络 (CRNN) 来识别运动员号牌。此外，为了提高检测的准确性，我们使用迁移学习来微调 CRNN。最后，我们设计了一种有效的树过滤算法来避免文本检测模块带来的干扰。它可以过滤掉无效的结果，从而提高模型的准确性。我们的模型能够对马拉松运动员的照片进行高精度分类。实验结果表明，该模型是可行且有效的。我们设计了一种有效的树过滤算法来避免文本检测模块带来的干扰。它可以过滤掉无效的结果，从而提高模型的准确性。我们的模型能够对马拉松运动员的照片进行高精度分类。实验结果表明，该模型是可行且有效的。我们设计了一种有效的树过滤算法来避免文本检测模块带来的干扰。它可以过滤掉无效的结果，从而提高模型的准确性。我们的模型能够对马拉松运动员的照片进行高精度分类。实验结果表明，该模型是可行且有效的。