能源研究与管理2026，Vol.18Issue(3)：45-53,9.DOI:10.16056/j.2096-7705.2026.03.005
CNN与Point-Net在不同训练数据比例下辐射异常识别分析
Analysis of Radiation Anomaly Identification Using CNN and Point-Net with Different Training Data Proportions
摘要
Abstract
To address the difficulty of directly reflecting ground anomaly details in airborne gamma-ray spectrometry measurements,this paper introduces both Point-Net and convolutional neural networks(CNN)into the task of nuclear environmental radiation anomaly identification.Based on ground and airborne measurement data acquired 17 months after the Fukushima Daiichi Nuclear Power Plant accident,the performance of the two types of algorithms is evaluated under different training data proportions.The results show that,after five independent training sessions,as the training proportion increases from 10％to 70％,the average prediction accuracy of Point-Net increases from 58.22％to 88.03％,outperforming CNN by 10.91％,3.71％,7.75％,and 9.70％under the four respective proportions,while its average loss values are consistently lower than those of CNN.At the 10％proportion,both algorithms tend to misclassify environmental background as low-value anomalies.At the 30％proportion,the predictions of Point-Net are already close to the ground-truth labels,whereas CNN exhibits an over identification of high-value anomaly regions.At the 50％proportion,Point-Net achieves good identification results for high value anomaly regions,with a correct recognition rate for high value anomalies that is 21.13％higher than that of CNN.Meanwhile,CNN still misclassifies 38.51％of environmental background samples as low-value anomalies.At the 70％proportion,the correct recognition rate of Point-Net for high-value anomalies reaches 97.86％,which is 6.26％higher than that of CNN.These findings confirm that Point-Net is more suitable for processing irregular radiation monitoring data and can provide a reference for anomaly identification and the required training data proportion in nuclear emergency environments.关键词
异常识别/卷积神经网络(CNN)/Point-Net/福岛第一核电站Key words
anomaly identification/convolutional neural networks(CNN)/Point-Net/Fukushima Daiichi Nuclear Power Plant分类
能源科技引用本文复制引用
吴囿昂,吴和喜,梁益..CNN与Point-Net在不同训练数据比例下辐射异常识别分析[J].能源研究与管理,2026,18(3):45-53,9.基金项目
国家自然科学基金(12265003) （12265003）
