Scene text detection using enhanced Extremal region and convolutional neural network

Naiemi, Fatemeh; Ghods, Vahid; Khalesi, Hassan

doi:10.1007/s11042-020-09318-2

Cited by 11 publications

(3 citation statements)

References 35 publications

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“…The PR curves 76 are able to show the detection results in more detail than the evaluation metrics 77 . The PR curves for these models are shown in Fig.…”

Section: Methodsmentioning

confidence: 99%

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Low saliency crack detection based on improved multimodal object detection network: an example of wind turbine blade inner surface

Gao

Dai

et al. 2023

J. Electron. Imag.

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.Accurate identification of cracks is of great significance for maintaining the health of the equipment. However, the low saliency of cracks in some composite or metal surfaces affects the detection accuracy of object detection algorithms. For example, small cracks on the inner surface of wind turbine blade (WTB) may be similar in color to the substrate or face complex background textures. Taking WTB cracks as low saliency crack samples, we propose a multimodal object detection convolutional neural network that fuses infrared images with visible images to detect cracks more accurately. The proposed network contains the CenterNet network with an existing fast and efficient mid-level fusion structure. First, we optimized the fusion structure to make it more suitable for extracting crack features. To address the problem that severe background interference in multimodal images affects the detection performance, we add channel attention to the fusion structure and train the improved network using a stepwise training method to enhance the framework’s ability to filter background interference information. Finally, the effectiveness of the improvements was verified by ablation experiments and feature map analysis, and the phenomena of wrong detection, missed detection, and repeated detection were reduced. The evaluation results show that the proposed multimodal object detection network is able to detect the low saliency WTB cracks more effectively, and the improvement of the network also results in a 6.22% increase in average precision. In addition, this method can be extended to other materials or scenes to identify very inconspicuous objects, replacing manual inspection in more challenging defect detection tasks.

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“…The PR curves 76 are able to show the detection results in more detail than the evaluation metrics 77 . The PR curves for these models are shown in Fig.…”

Section: Methodsmentioning

confidence: 99%

“…However, the detection performance of both MLF and MMLF decreased after the addition of CA. The PR curves 76 are able to show the detection results in more detail than the evaluation metrics. 77 The PR curves for these models are shown in Fig.…”

Section: Ablation Experimentsmentioning

confidence: 99%

Low saliency crack detection based on improved multimodal object detection network: an example of wind turbine blade inner surface

Gao

Dai

et al. 2023

J. Electron. Imag.

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show abstract

“…Image mapping to distinct color channels red, green, and blue. Smoothing, noise elimination, and setting the image contrast [13]. The block diagram in Figure 4 shows that.…”

Section: Pre-processingmentioning

confidence: 99%

Scene Text Recognition: A Review

Mageed

Abbadi

2023

Iraqi Journal of Science

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The problem of text recognition and its applicability as part of images captured in the wild has gained a significant attention from the computer vision community in recent years. In contrast to the recognition of printed documents, scene text recognition is a difficult problem. Contrary to recognition of printed documents, recognizing a scene text is a challenging problem. Many researches focus on the problem of recognizing text extracted from natural scene images. Significant attempts have been made to address this problem in recent past. However, many of these attempts work on utilizing availability of strong context, which naturally limits the dictionary. This paper presents a review of recent papers related to scene text recognition in the period (2013-2020). This paper helps other researchers to understand the whole system of scene text recognition instead of reading many papers in isolation.

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Text Region Identification from Natural Scene Images Using Semi-Supervised MSER Method

Das¹,

Chattopadhyay²,

Prasad³

et al. 2022

Proceedings of 2nd International Conference on Mathematical Modeling and Computational Science

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Scene text detection using enhanced Extremal region and convolutional neural network

Cited by 11 publications

References 35 publications

Low saliency crack detection based on improved multimodal object detection network: an example of wind turbine blade inner surface

Low saliency crack detection based on improved multimodal object detection network: an example of wind turbine blade inner surface

Scene Text Recognition: A Review

Text Region Identification from Natural Scene Images Using Semi-Supervised MSER Method

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