Department of Signal Processing and Pattern Recognition, Institute of Computer Science, Pedagogical University of Krakow, 2 Podchorazych Ave, 30-084 Krakow, Poland.
Department of Computer Physics and Quantum Informatics, Institute of Computer Science, Pedagogical University of Krakow, 2 Podchorazych Ave, 30-084 Krakow, Poland.
Sensors (Basel). 2021 Mar 11;21(6):1963. doi: 10.3390/s21061963.
In this paper, we describe the convolutional neural network (CNN)-based approach to the problems of categorization and artefact reduction of cosmic ray images obtained from CMOS sensors used in mobile phones. As artefacts, we understand all images that cannot be attributed to particles' passage through sensor but rather result from the deficiencies of the registration procedure. The proposed deep neural network is composed of a pretrained CNN and neural-network-based approximator, which models the uncertainty of image class assignment. The network was trained using a transfer learning approach with a mean squared error loss function. We evaluated our approach on a data set containing 2350 images labelled by five judges. The most accurate results were obtained using the VGG16 CNN architecture; the recognition rate (RR) was 85.79% ± 2.24% with a mean squared error (MSE) of 0.03 ± 0.00. After applying the proposed threshold scheme to eliminate less probable class assignments, we obtained a RR of 96.95% ± 1.38% for a threshold of 0.9, which left about 62.60% ± 2.88% of the overall data. Importantly, the research and results presented in this paper are part of the pioneering field of the application of citizen science in the recognition of cosmic rays and, to the best of our knowledge, this analysis is performed on the largest freely available cosmic ray hit dataset.
在本文中，我们描述了一种基于卷积神经网络（CNN）的方法，用于对手机中使用的 CMOS 传感器获得的宇宙射线图像进行分类和减少伪影。我们将伪影理解为所有不能归因于粒子穿过传感器的图像，而是源于注册过程缺陷的图像。所提出的深度神经网络由预训练的 CNN 和基于神经网络的逼近器组成，该逼近器对图像类分配的不确定性进行建模。该网络使用具有均方误差损失函数的迁移学习方法进行训练。我们使用包含 2350 张由五位评委标记的图像的数据集来评估我们的方法。使用 VGG16 CNN 架构获得了最准确的结果；识别率（RR）为 85.79%±2.24%，均方误差（MSE）为 0.03±0.00。在应用所提出的阈值方案消除不太可能的类分配后，对于阈值为 0.9，我们获得了 96.95%±1.38%的 RR，这使得大约 62.60%±2.88%的总体数据被保留。重要的是，本文中呈现的研究和结果是公民科学在宇宙射线识别中的应用的开创性领域的一部分，据我们所知，这是在最大的免费可用宇宙射线命中数据集上进行的分析。
