摘要
针对当前安全帽检测准确性低和适应性差的问题,提出一种以YOLOv3网络为基础,进行相应改进的安全帽检测方法;为了保证安全帽检测的准确度和增大对图片中安全帽的关注度,采用注意力机制增强了从图片提取出的空间信息和语义信息,减少了图像细节的丢失,再使用可变卷积来适应人的姿态变化,增强了模型对目标的适应性,减少了一定量的训练样本,最后通过改变输出特征图的尺寸,融合浅层的网络特征,提升了人头等小目标的识别率;采用自制的HELMET数据集对方法进行训练与测试,并通过对比实验表明:方法相较于其他检测方法能够提取到更多的目标特征,达到更高的平均精度均值,同时在实际应用中适应性较好。
In order to solve the problem of low accuracy and poor adaptability of helmet detection,the author proposed an improved helmet detection method based on YOLOv3 network.Aiming at ensuring the accuracy of helmet detection and increasing the attention to the helmet in the picture,this method used attention mechanism to enhance the spatial information and semantic information extracted from the picture,and reduced the loss of image details.And then the author used deformable convolution to adapt to the variety of human posture,enhance the adaptability of the model to the target,and reduce a few of training samples.In the end,the author changes the size of output feature map and the shallow network features are integrated to improve the recognition rate of small targets such as human head.The self-made HELMET dataset is used to train and test the method,and the comparative experiments show that compared with other detection methods,this method can extract more target features,achieve higher mean average accuracy,and has better adaptability in practical application.
作者
张学锋
王子琦
汤亚玲
ZHANG Xue-feng;WANG Zi-qi;TANG Ya-ling(School of Computer Science and Technology, Anhui University of Technology, Anhui Maanshan 243000, China)
出处
《重庆工商大学学报（自然科学版）》
2022年第4期32-41,共10页
Journal of Chongqing Technology and Business University:Natural Science Edition
基金
安徽省教育厅重大课题基金项目(KJ2017ZD05)
安徽高校协同创新项目(GXXT-2019-008)
结合实物机器人的化工企业救援仿真系统(TZJQR002-2021).
关键词
安全帽检测
注意力机制
可变卷积
特征图
safety helmet detection
attention mechanism
deformable convolution
feature map
