应用改进YOLOv5s的转炉下渣状态检测算法研究
Research on the state detection algorithm of slag in converter based on improved YOLOv5s
曹君 1李爱莲 1解韶峰 2崔桂梅1
作者信息
- 1. 内蒙古科技大学信息工程学院,内蒙古包头 014010
- 2. 内蒙古科技大学基建处,内蒙古包头 014010
- 折叠
摘要
针对人工目测法、红外热像检测法在转炉下渣状态检测中检测精度和实时性较差带来挡渣操作不及时,进而影响钢成品质量的问题,提出一种基于改进YOLOv5s的转炉下渣状态检测方法.在主干网络融合卷积注意力(CBAM),增强算法特征提取能力;在颈部层引入加权双向特征金字塔结构(BiFPN),将主干结构的原始特征信息与输出节点的特征信息进行多层次融合,并给予不同特征相应的权重,获得更加丰富的特征图;在检测层使用EIoU Loss函数优化模型性能,提升预测框的收敛速度.实验结果表明:改进后模型的均值平均精度(mAP)达到 91.8%,每秒传输帧数(FPS)为87.7 f/s,相比原模型分别提高4.6%和11.4%.
Abstract
Aiming at the problem of untimely slag blocking operation affects the quality of steel products caused by poor detection accuracy and real-time performance of manual visual inspection method and infrared thermal imaging detection method in the detection of converter slag state,an improved YOLOv5s-based convert slag state detection method is proposed.Integrating the convolutional block attention module(CBAM)in the backbone network to enhance the feature extraction performance of the algorithm.The bidirectional feature pyramid network(BiFPN)is applied in the neck layer,the original feature information of the backbone structure and the feature information of the output nodes are multi-levelly fused,and corresponding weights are given to different features to obtain richer feature-maps.The EIoU Loss function is used in the detection layer to optimize the model performance and improve the convergence speed of the prediction frame.Experimental results show that the mean average precision(mAP)of the improved YOLOv5s reaches 91.8%,and the number of frames per second(FPS)is 87.7 f/s,which are 4.6%and 11.4%higher than the original algorithm.
关键词
下渣检测/YOLOv5s/注意力机制/损失函数/BiFPNKey words
slag detection/YOLOv5s/attention mechanism/loss function/BiFPN引用本文复制引用
基金项目
国家自然科学基金资助项目(61763039)
内蒙古自治区自然科学基金项目资助(2022MS06003)
出版年
2024
NETL
NSTL
万方数据