基于自适应特征融合的低质量钢印字符检测和识别.
Saved in:
| Title: | 基于自适应特征融合的低质量钢印字符检测和识别. |
|---|---|
| Alternate Title: | Low-quality steel stamp character detection and recognition based on adaptive feature fusion. |
| Authors: | 吕淑静1 sjlv@cs.ecnu.edu.cn, 娄鹏杰1 479168158@qq.com, 彭世全2 583151823@qq.com, 赵春龙2 278763845@qq.com |
| Source: | Computer Engineering & Science / Jisuanji Gongcheng yu Kexue. Apr2026, Vol. 48 Issue 4, p699-708. 10p. |
| Subjects: | Pattern recognition systems, Feature extraction, Wheels, Lettering, Steel industry |
| Abstract (English): | To address the challenges faced by stamp character detection on metal products, such as character tilt, blurriness, inconsistent fonts, and interference from rust stains, a character detection model based on adaptive feature fusion, named YOLO-CHAR, is proposed. This model employs the MobileNet feature extraction network to dynamically adjust the weights of channel features, enhancing the model's ability to capture key features. At the feature fusion layer, it utilizes the generalized feature pyramid network(GFPN) structure and the simplified attention module(SimAM) attention mechanism to flexibly capture multi-scale features and strengthen feature fusion capabilities. Based on this character detection model, a low-quality train wheelsets stamp character detection and recognition system is designed and implemented. This system has been put into use, achieving an overall daily average recognition accuracy of over 92% for wheelsets, which meets the on-site operational requirements. [ABSTRACT FROM AUTHOR] |
| Abstract (Chinese): | 针对金属制品钢印所面临的字符倾斜、模糊、字体不统一及铁锈污渍干扰等问题,提出一种基 于自适应特征融合的字符检测模型YOLO-CHAR,采用MobileNet特征提取网络动态调整通道特征权 重,增强模型对关键特征的捕捉能力,在特征融合层采用GFPN 网络结构和SimAM 注意力机制,灵活捕 捉多尺度特征并加强特征融合能力;基于该字符检测模型,设计并实现了一套低质量火车轮轴钢印字符检 测识别系统,该系统已投入使用,轮轴的整体识别日均准确率达到92%以上,满足现场使用要求。. [ABSTRACT FROM AUTHOR] |
| Copyright of Computer Engineering & Science / Jisuanji Gongcheng yu Kexue is the property of Computer Engineering & Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
Be the first to leave a comment!
