数据采集与处理2024，Vol.39Issue(5)：1228-1239,12.DOI:10.16337/j.1004-9037.2024.05.014
基于改进级联R-CNN的钢材带状碳化物检测与分级
Detection and Classification of Banded Carbide in Steel Based on Improved Cascade R-CNN
郝亮 1周诗洋 1莫允扬 1陈勇勇 1徐勇 1苏敬勇1
作者信息
- 1. 哈尔滨工业大学(深圳)计算机科学与技术学院,深圳 518055
- 折叠
摘要
Abstract
In the steel industry,carbide is a vital constituent,whose distribution in steel materials holds significant reference value for evaluating steel quality.However,the current detection methods for carbide in steel bars primarily rely on manual inspection,which is costly and lacks stability.This study introduces advanced deep learning techniques from the domain of artificial intelligence,which collects and annotates 3 192 high quality images of banded carbides on steel bars,alongside 11 complete samples to create a banded carbide dataset on object detection for steel bars(BCDOD).Common deep learning methods for object detection are applied to the dataset through experimental analysis.With a focus on the specific characteristics of the application scenario and data,the cascade R-CNN model is enhanced with rotation data augmentation,improvement to the Focal Loss function and negative sample fine-tuning,resulting in performance improvement.The achieved average precision reaches 96%,with 100%recognition accuracy on complete sample data,showcasing promising outcomes that address the existing gap in artificial intelligence technology within the field of carbide metallographic detection.关键词
碳化物/金相组织/缺陷检测/目标检测/级联R-CNNKey words
carbide/metallographic structure/defect detection/object detection/cascade R-CNN分类
信息技术与安全科学引用本文复制引用
郝亮,周诗洋,莫允扬,陈勇勇,徐勇,苏敬勇..基于改进级联R-CNN的钢材带状碳化物检测与分级[J].数据采集与处理,2024,39(5):1228-1239,12.
