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Research on text detection method based on improved yolov3

机译：基于改进YOLOV3的文本检测方法研究

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Text detection in natural scenes is one of the hot issues in the field of computer vision. Aiming at the problems of complex text background, object occlusion and illumination changes in natural scenes, this paper proposes a method of text detection in natural scenes based on improved YOLOv3 (UDSP- YOLO), this method uses the CLAHE image enhancement preprocessing method to eliminate the impact of lighting changes in the natural scene on the target recognition effect, and uses random spatial sampling pooling (S3Pool) as the downsampling method of the feature extraction network to preserve the space of the feature map Information solves the problem of background interference in complex environments. The 8 times down-sampling feature map output by the feature extraction network is up-sampled by 2 times, and the 2 times up-sampling feature map is spliced with the feature map output by the second residual block to establish The output is a 4 times downsampling feature fusion target detection layer, and the improved network model is used to compare experiments with the original network on the ICDAR2015 data set. The results show that the improved network model effectively improves the detection accuracy.
机译：自然场景中的文本检测是计算机视野领域的热点问题之一。针对复杂文本背景，对象闭塞和照明的自然场景的变化，本文提出了一种基于改进的YOLOV3（UDSP-YOLO）的自然场景中的文本检测方法，这种方法使用CLAHE图像增强预处理方法来消除照明变化在目标识别效果上的影响，并使用随机空间采样池（S3POOL）作为特征提取网络的下采样方法，以保护特征图信息的空间解决了复杂的背景干扰的问题环境。特征提取网络输出的8次下采样功能映射由2次上采样，2次上采样功能映射与第二个残差块输出的特征映射拼接，以建立输出是4时间下采样特征融合目标检测层，并且改进的网络模型用于将实验与ICDAR2015数据集上的原始网络进行比较。结果表明，改进的网络模型有效提高了检测精度。

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