在钢铁行业中,碳化物是钢材中一种非常重要的组成成分,其在钢材中的分布对于评估钢材的质量具有很高的参考价值.然而,目前棒材碳化物的检测手段主要为人工检测,成本高昂且缺乏稳定性.引入人工智能领域的深度学习技术,收集并标注了3 192张高质量钢铁棒材带状碳化物图像与11个完整样品数据,创建了工业场景下的棒材带状碳化物目标检测数据集(Banded carbide dataset on object detection for steel bar,BCDOD).使用深度学习领域中常见的目标检测方法对数据集进行了实验分析,针对应用场景与数据的特点,引入旋转数据增强、Focal Loss函数与负样本微调对级联R-CNN模型进行改进,提升了模型的性能,平均精度达到96%.同时,在完整样品数据取得了100%的识别准确率,取得了较为理想的效果,弥补了人工智能技术在碳化物金相检测领域的空缺.
Detection and Classification of Banded Carbide in Steel Based on Improved Cascade R-CNN
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.
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