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Robust meter reading detection via differentiable binarization
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DOI：10.1007/s10489-024-05278-4.png)
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摘要
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Recently, scene text detection based on pixel-level segmentation becomes quite popular in industry, which is suitable for real-time digital meter reading detection. However, detection results of existing methods are usually not satisfactory due to the poor quality of images captured in a complex environment. In this paper, a novel method named Retina Differentiable Binarization (RetinaDB) is proposed for digital meter reading detection, which is robust to low quality images including blur, out-of-focus, shadow and so on. Bottom-up path augmentation and fusion factor are adopted into our feature pyramid network module to enhance the robustness of segmentation network, and an adaptive binarization function is designed for a better approximation to the standard binarization function. The experimental results, obtained on ICDAR 2015 and a digital meter reading dataset, show that the proposed RetinaDB significantly outperforms existing methods in both accuracy and efficiency. In particular, on the digital meter reading dataset, our detector achieves an F1-measure of 97.61%, inferencing at 47.13 FPS when using ResNet-18 as a backbone. When using ResNet-50, our method achieves the best detection results of 98.79%, 99.58% and 98.01% in terms of F1-measure, precision and recall.
Keyword:
Meter reading detection
Feature fusion
Deep neural networks
Differentiable binarization
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3.5
论文数:
7.6K
被引数:
1.7W
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