This paper presents our proposed methods to ICDAR 2021 Robust Reading Challenge - Integrated Circuit Text Spotting and Aesthetic Assessment (ICDAR RRC-ICTEXT 2021). For the text spotting task, we detect the characters on integrated circuit and classify them based on yolov5 detection model. We balance the lowercase and non-lowercase by using SynthText, generated data and data sampler. We adopt semi-supervised algorithm and distillation to furtherly improve the model's accuracy. For the aesthetic assessment task, we add a classification branch of 3 classes to differentiate the aesthetic classes of each character. Finally, we make model deployment to accelerate inference speed and reduce memory consumption based on NVIDIA Tensorrt. Our methods achieve 59.1 mAP on task 3.1 with 31 FPS and 306M memory (rank 1), 78.7\% F2 score on task 3.2 with 30 FPS and 306M memory (rank 1).
翻译:本文介绍了我们提议的ICDAR 2021强读挑战-综合电路文本显示和审美评估(ICDAR RRC-ICTEXT 2021)的方法。对于文本定位任务,我们检测集成电路上的字符,并根据Yolov5探测模型对其进行分类。我们通过使用合成图文、生成的数据和数据取样器来平衡小写和非低写。我们采用了半监督算法和蒸馏法,以进一步提高模型的准确性。对于美学评估任务,我们添加了3个类别的分类分支,以区分每个字符的审美类别。最后,我们根据NVIDIA Tensorrt进行模型部署,以加速推算速度并减少内存消耗。我们的方法在任务3.1上实现了59.1兆帕, 31 FPS 和 306M 内存(第1级),78.7 ⁇ F2在任务3.2中得分,30 FPS 和 306M 内存(第1级)。