摘要
针对视觉测量中低照度或复杂背景下圆形合作目标识别精度较低或定位能力较差等问题,提出了一种基于卷积神经网络(Convolutional Neural Networks,CNNs)对YOLOv8算法进行优化设计的模型。该模型共有225层网络、约300万个参数以及8.2G FLOPs计算量。采用不同条件下的圆形合作目标数据集进行模型训练,同时在训练过程中实时监控模型的性能指标和计算效率,对模型进行细致的调整和优化。实验结果表明,本文算法具有99%的准确率、92%的召回率、92%的平均精度。相较于霍夫变换和YOLOv3等传统特征提取方法,本文算法的精确率分别提升了14%和4%,召回率分别提升了17%和2%,平均精度分别提升了10%和2%。该算法可以在低照度环境、复杂背景或目标形状微小变化等多变条件下,显著提高圆形合作目标的识别定位精度。
Aiming at problems such as low recognition accuracy or poor localization ability of circular cooperative objects in low illumination or complex backgrounds in vision measurement,a model based on CNNs is proposed in this paper to optimize the YOLOv8 algorithm.The model designed in this paper has a total of 225 layers of network,about 3 million parameters and 8.2G FLOPs of computing power.The model is trained by using the circular cooperative target data set under different conditions,and the performance index and computational efficiency of the model are monitored in real time during the training process,and the model is adjusted and optimized in detail.The experimental results show that the algorithm has a precision of 99%,a recall rate of 92%and an average accuracy of 92%.Compared with traditional feature extraction methods such as Hough transform and YOLOv3,the accuracy of the proposed algorithm is improved by 14%and 4%.Recall rates increase by 17%and 2%.The average accuracy is improved by 10%and 2%.The algorithm can significantly improve the recognition and positioning accuracy of circular cooperative targets under variable conditions such as low illumination environment,complex background or small change of target shape.
作者
徐非
林雪竹
郭丽丽
孙静
李丽娟
XU Fei;LIN Xue-zhu;GUO Li-li;SUN Jing;LI Li-juan(College of Opto-Electronic Engineering,Changchun University ofScience and Technology,Changchun 130022,China)
出处
《红外》
CAS
2024年第9期29-43,共15页
Infrared
基金
吉林省科技发展计划重点研发项目(20200401063GX)。
