Abstract
Texts on the intelligent transportation scene include mass information. Fully harnessing this information is one of the critical drivers for advancing intelligent transportation. Unlike the general scene, detecting text in transportation has extra demand, such as a fast inference speed, except for high accuracy. Most existing real-time text detection methods are based on the shrink mask, which loses some geometry semantic information and needs complex post-processing. In addition, the previous method usually focuses on correct output, which ignores feature correction and lacks guidance during the intermediate process. To this end, we propose an efficient multi-scene text detector that contains an effective text representation similar mask (SM) and a feature correction module (FCM). Unlike previous methods, the former aims to preserve the geometric information of the instances as much as possible. Its post-progressing saves 50$\%$ of the time, accurately and efficiently reconstructing text contours. The latter encourages false positive features to move away from the positive feature center, optimizing the predictions from the feature level. Some ablation studies demonstrate the efficiency of the SM and the effectiveness of the FCM. Moreover, the deficiency of existing traffic datasets (such as the low-quality annotation or closed source data unavailability) motivated us to collect and annotate a traffic text dataset, which introduces motion blur. In addition, to validate the scene robustness of the SM-Net, we conduct experiments on traffic, industrial, and natural scene datasets. Extensive experiments verify it achieves (SOTA) performance on several benchmarks. The code and dataset are available at: \url{this https URL}.
Abstract (translated)
智能交通场景中的文本包括大量信息，充分利用这些信息是推动智能交通发展的重要驱动力之一。与一般场景不同的是，在交通中检测文本还具有额外需求，除了高精度外还需要快速推理速度。现有的大多数实时文本检测方法都是基于缩放掩码的，这种方法会丢失一些几何语义信息，并且需要复杂的后处理。此外，之前的方法通常专注于正确输出，忽略了特征校正并在中间过程中缺乏指导。为此，我们提出了一种高效的多场景文本检测器，该检测器包含一种有效的文本表示相似掩码（SM）和一个特征校正模块（FCM）。与以前的方法不同的是，前者旨在尽可能保留实例的几何信息，其后处理节省了50%的时间，并且能够准确高效地重构文本轮廓。后者鼓励误报特征远离正样本特征中心，从特征级别优化预测结果。一些消融研究展示了SM的有效性和FCM的效果。此外，现有交通数据集（如低质量标注或封闭源数据不可用）的不足促使我们收集并标注了一个包含运动模糊的交通文本数据集。为了验证SM-Net在场景中的鲁棒性，我们在交通、工业和自然场景的数据集上进行了实验。广泛的实验证明它在多个基准测试中实现了最先进的（SOTA）性能。代码和数据集可在以下链接获取：\url{this https URL}。
URL
https://arxiv.org/abs/2411.02794
