Contour based scene text detection methods have rapidly developed recently,
but still suffer from inaccurate frontend contour initialization, multi-stage
error accumulation, or deficient local information aggregation. To tackle these
limitations, we propose a novel arbitrary-shaped scene text detection framework
named CT-Net by progressive contour regression with contour transformers.
Specifically, we first employ a contour initialization module that generates
coarse text contours without any post-processing. Then, we adopt contour
refinement modules to adaptively refine text contours in an iterative manner,
which are beneficial for context information capturing and progressive global
contour deformation. Besides, we propose an adaptive training strategy to
enable the contour transformers to learn more potential deformation paths, and
introduce a re-score mechanism that can effectively suppress false positives.
Extensive experiments are conducted on four challenging datasets, which
demonstrate the accuracy and efficiency of our CT-Net over state-of-the-art
methods. Particularly, CT-Net achieves F-measure of 86.1 at 11.2 frames per
second (FPS) and F-measure of 87.8 at 10.1 FPS for CTW1500 and Total-Text
datasets, respectively.

通过渐进式轮廓回归及轮廓变换器，我们提出了一种名为 CT-Net 的新型任意形状场景文本检测框架，以解决前端轮廓初始化不准确、多阶段误差累积或局部信息聚合不足的限制，并通过大量实验验证了其在准确性和效率方面超越了现有方法。

CT-Net: 通过轮廓变换器进行任意形状文本检测

CT-Net: Arbitrary-Shaped Text Detection via Contour Transformer

We propose a new method for testing antenna arrays that records the radiating
electromagnetic (EM) field using an absorbing material and evaluating the
resulting thermal image series through an AI using a conditional
encoder-decoder model. Given the power and phase of the signals fed into each
array element, we are able to reconstruct normal sequences through our trained
model and compare it to the real sequences observed by a thermal camera. These
thermograms only contain low-level patterns such as blobs of various shapes. A
contour-based anomaly detector can then map the reconstruction error matrix to
an anomaly score to identify faulty antenna arrays and increase the
classification F-measure (F-M) by up to 46%. We show our approach on the time
series thermograms collected by our antenna testing system. Conventionally, a
variational autoencoder (VAE) learning observation noise may yield better
results than a VAE with a constant noise assumption. However, we demonstrate
that this is not the case for anomaly detection on such low-level patterns for
two reasons. First, the baseline metric reconstruction probability, which
incorporates the learned observation noise, fails to differentiate anomalous
patterns. Second, the area under the receiver operating characteristic (ROC)
curve of a VAE with a lower observation noise assumption achieves 11.83% higher
than that of a VAE with learned noise.

本研究提出使用吸收材料记录辐射电磁场并通过条件编码器 - 解码器 AI 评估生成热图像序列的新方法，进行天线阵列的故障检测。研究表明，在低级图案下，基于轮廓的异常检测可以将重构误差矩阵映射到异常评分，以提高分类 F-M 度量达 46％。