优化Swin Transformer的塔式起重机销轴安全状态识别算法

doi:10.16265/j.cnki.issn1003-3033.2023.09.0173

中国安全科学学报 ›› 2023, Vol. 33 ›› Issue (9): 63-68.doi: 10.16265/j.cnki.issn1003-3033.2023.09.0173

优化Swin Transformer的塔式起重机销轴安全状态识别算法

周庆辉¹^,²(), 刘浩世¹^,^**()

¹ 北京建筑大学机电与车辆工程学院,北京,100044
² 北京市建筑安全监测工程技术研究中心,北京,100044

收稿日期:2023-03-21 修回日期:2023-06-28 出版日期:2023-09-28
通讯作者:
**刘浩世(1998—),男,山东青岛人,硕士研究生,主要研究方向为安全工程、人工智能、深度学习。E-mail:358840846@qq.com。
作者简介:
周庆辉 (1973—),男,山东济南人,博士,副教授,主要从事起重机智能检测技术研究。E-mail:zhouqinghui@bucea.edu.cn。
基金资助:
国家自然科学基金青年基金资助(51905028); 住房和城乡建设部科技计划项目(2022-K-079)

Recognition algorithm on safe states of tower crane pins based on optimized Swin Transformer

ZHOU Qinghui¹^,²(), LIU Haoshi¹^,^**()

¹ School of Mechanical-Electrical and Vehicle Engineering, Beijing University of Civil Engineering and Architecture, Beijing 100044,China
² Beijing Engineering Research Center of Monitoring for Construction Safety, Beijing 100044,China

Received:2023-03-21 Revised:2023-06-28 Published:2023-09-28

摘要/Abstract

摘要：

为减小塔式起重机运行安全隐患,提高机器视觉检验销轴连接状态的准确率,提出一种优化 Swim Transformer的塔式起重机销轴安全状态识别算法;首先通过采集工地现场塔式起重机销轴安全状态图像,创建数据集;其次对数据集中销轴安全状态分类,并进行独热编码;然后基于Swin Transformer算法,建立销轴安全状态的识别模型,构造和优化损失函数;再运用AdamW优化器更新梯度,经过1 000次训练迭代后得到最终模型;最后在所创建的销轴图像数据集上,进行试验验证。结果表明:所提优化算法提高了塔式起重机销轴安全状态识别能力:准确率为99.4%、平均精度为99.4%,平均召回率为99.4%,平均特异度为99.6%,呈现出良好的分类和泛化能力;同时明显优于ShuffleNet、DenseNet和EfficientNet等3种典型算法;与原Swin Transformer算法相比,准确率也提高了3.6%。

关键词: Swin Transformer, 塔式起重机, 销轴, 安全状态, 状态识别, 数据集

Abstract:

In order to reduce the hidden danger during tower crane operation, and improve the accuracy of machine vision when identifying states of pins, a recognition algorithm was proposed based on an optimized Swin Transformer. Firstly, A data set was created by collecting the pinned images of the tower crane on the construction site. Secondly, the safe state of pins was classified and encoded using the One-hot-coding method in the data set. Then, a recognition model for the safety status of pins was established based on an optimized Swin Transformer in which the loss function was adjusted. Through updating the gradient by the AdamW optimizer, a final model was obtained after 1000 training iterations. Finally, an experimental verification was conducted on the pinned image dataset. The results show that the proposed method can improve the identification ability of the safe state of tower crane pins, and its accuracy can reach 99.4%. The average accuracy, the average recall rate and the average specificity can reach 99.4%, 99.4%, and 99.6%, respectively. Its accuracy is higher than typical algorithms, such as DenseNet, ShuffleNet and EfficientNet. When opposed to the original Swin Transformer, the accuracy is also added by 3.6%.

Key words: Swin Transformer, tower crane, pins, safety state, state identification, data set

周庆辉, 刘浩世. 优化Swin Transformer的塔式起重机销轴安全状态识别算法[J]. 中国安全科学学报, 2023, 33(9): 63-68.

ZHOU Qinghui, LIU Haoshi. Recognition algorithm on safe states of tower crane pins based on optimized Swin Transformer[J]. China Safety Science Journal, 2023, 33(9): 63-68.

图/表 10

图1

图2

表1

表2

表3

表4

评价指标计算公式

评价指标	公式
A	A= $T P + T N T P + T N + F P + F N$
P	P= $T P T P + F P$
R	R= $T P T P + F N$
S	S= $T N T N + F P$
AP	AP= $P 1 + P 2 + … + P N N$

表4

图3

图4

图5

表5

参考文献 17

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销轴状态	总样本数	训练样本	测试样本
不安全状态	376	301	75
安全状态	733	587	146
较不安全状态	452	362	90

销轴状态	类别	编码
不安全状态	无开口销或者由铁丝替代	100
正常安全状态	正常的开口销	010
较不安全状态	开口销开口过小	001

混淆矩阵		真实值
混淆矩阵		真值	假值
预测值	真值	TP	FP
预测值	假值	FN	TN

评价指标	不安全状态	正常安全状态	较不安状态	A	AP
优化后	—	—	—	0.994	0.994
P	1.000	0.993	0.989	—	—
R	1.000	0.993	0.989	—	—
S	1.000	0.994	0.995	—	—
ShuffleNet	—	—	—	0.833	0.851
P	0.914	0.794	0.845	—	—
R	0.853	0.925	0.667	—	—
S	0.975	0.788	0.950	—	—
DenseNet	—	—	—	0.916	0.927
P	0.971	0.893	0.916	—	—
R	0.893	0.973	0.844	—	—
S	0.992	0.897	0.968	—	—
EfficientNet	—	—	—	0.936	0.937
P	0.960	0.932	0.920	—	—
R	0.960	0.945	0.900	—	—
S	0.987	0.939	0.968	—	—
Swin Transformer	—	—	—	0.958	0.961
P	1.000	0.959	0.924	—	—
R	0.973	0.959	0.944	—	—
S	1.000	0.964	0.968	—	—

优化Swin Transformer的塔式起重机销轴安全状态识别算法

Recognition algorithm on safe states of tower crane pins based on optimized Swin Transformer

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