基于改进YOLOv5的复杂环境下小目标弹孔识别
Small target bullet hole recognition in complex environment based on modified YOLOv5
吴飞 1李纶1
作者信息
- 1. 武汉理工大学机电工程学院,武汉 430000
- 折叠
摘要
为实现能够在不同光照、阴影遮挡、弹孔重叠等复杂环境下对靶面弹孔的高效精准地识别,提出一种基于改进YOLOv5 的实例分割算法.模型以YOLOv5 主干网络为基础,融合了含有ASPP模块的分割解码器,实现对弹孔小目标的实例分割;对目标检测端输出参数进行解耦,降低回归参数与类别概率的耦合影响,提高识别精度;调整模型的输出尺度,删减预测大目标的特征层,增加融合低层信息的极小目标预测层,以此提升检测召回率与精确率.对于经过数据集训练后的模型,在测试集下的检测精度达到 92.42%,检测速度达到 25.15 帧/s.与原网络YOLOv5、Mask RCNN、Deeplabv3 +等网络的对比试验表明,所提模型在复杂环境下具有较高的检测精度与检测速度,满足实际训练中的精确性与实时性要求.
Abstract
In order to recognize bullet holes on target surface efficiently and accurately in complex environments such as different illumination,shadow occlusion and overlap of bullet holes,an instance segmentation algorithm based on improved YOLOv5 was proposed.Based on the YOLOv5 backbone network,the model integrated the segmentation decoder with Atrous Spatial Pyramid Pooling module to realize the instance segmentation of small bullet holes.The algorithm added decoupled detect head to reduce the coupling effect of regression parameters and category probability and improved the recognition accuracy.In addition,the output scale of the mode was adjusted by deleting the large-scale forecasting feature layer and adding the minimum-scale prediction layer that fused low-level information to improve the detection recall rate and accuracy of bullet holes.For the model trained by the data set,the detection accuracy under the test set reaches 92.42%,and the detection speed reaches 25.15 frames/s.Compared with the original network YOLOv5,Mask RCNN,Deeplabv3 +and other networks,the proposed model has higher detection accuracy and detection speedin complex environments and meets the requirements of accuracy and real-time in actual training.
关键词
深度学习/实例分割/YOLOv5/弹孔识别/复杂环境Key words
deep learning/instance segmentation/YOLOv5/bullet hole recognition/complex environ-ment引用本文复制引用
出版年
2024
NETL
NSTL
万方数据