用追踪思路解决文本检测的后处理难题，精度速度双提升。
Explicit Relational Reasoning Network for Scene Text Detection
- 将文本组件看作序列对象，端到端建模关系，无需后处理。
- 在多个基准上达到领先精度，推理速度快于同类方法。
- 引入位置监督分类损失，解决置信度与定位不一致问题。
连通域（CC）是符合人类阅读直觉的文本形状表示方式，但基于CC的文本检测方法近年陷入发展瓶颈——其耗时的后处理难以消除。为此，本文提出显式关系推理网络（ERRNet），无需后处理即可优雅建模组件间关系。具体地，将每个文本实例表示为有序的多个文本组件，并将其视为序列运动中的物体，从而将场景文本检测创新性地视为一个追踪问题。基于此视角，设计了一个端到端追踪解码器，实现完全摒弃后处理的基于连通域的方法。此外，观察到分类置信度与定位质量之间存在不一致性，提出一种快速准确的多边形蒙特卡洛评估方法，并引入位置监督分类损失，以指导任务对齐学习。在多个挑战性基准上的实验表明，所提方法在保持高推理速度的同时，持续达到最先进的检测精度。
原文摘要 · Abstract (English)
Connected component (CC) is a proper text shape representation that aligns with human reading intuition. However, CC-based text detection methods have recently faced a developmental bottleneck that their time-consuming post-processing is difficult to eliminate. To address this issue, we introduce an explicit relational reasoning network (ERRNet) to elegantly model the component relationships without post-processing. Concretely, we first represent each text instance as multiple ordered text components, and then treat these components as objects in sequential movement. In this way, scene text detection can be innovatively viewed as a tracking problem. From this perspective, we design an end-to-end tracking decoder to achieve a CC-based method dispensing with post-processing entirely. Additionally, we observe that there is an inconsistency between classification confidence and localization quality, so we propose a Polygon Monte-Carlo method to quickly and accurately evaluate the localization quality. Based on this, we introduce a position-supervised classification loss to guide the task-aligned learning of ERRNet. Experiments on challenging benchmarks demonstrate the effectiveness of our ERRNet. It consistently achieves state-of-the-art accuracy while holding highly competitive inference speed.
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