计算机工程与科学2026，Vol.48Issue(4)：699-708,10.DOI:10.3969/j.issn.1007-130X.2026.04.014
基于自适应特征融合的低质量钢印字符检测和识别
Low-quality steel stamp character detection and recognition based on adaptive feature fusion
吕淑静 1娄鹏杰 1彭世全 2赵春龙 2刘运丹 2吕岳1
作者信息
- 1. 华东师范大学上海市多维度信息处理重点实验室,上海 200241
- 2. 齐齐哈尔四达铁路设备有限责任公司,黑龙江 齐齐哈尔 161000
- 折叠
摘要
Abstract
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 de-signed and implemented.This system has been put into use,achieving an overall daily average recogni-tion accuracy of over 92%for wheelsets,which meets the on-site operational requirements.关键词
文本检测/文本识别/钢印字符/自适应特征融合/注意力机制Key words
text detection/text recognition/steel stamp character/adaptive feature fusion/attention mechanism分类
信息技术与安全科学引用本文复制引用
吕淑静,娄鹏杰,彭世全,赵春龙,刘运丹,吕岳..基于自适应特征融合的低质量钢印字符检测和识别[J].计算机工程与科学,2026,48(4):699-708,10.
