Abstract
In the task of detecting craters on the lunar surface, some craters were difficult to detect correctly, and a Deep Neural Network (DNN) could not represent the uncertainty of such detection. However, a measure of uncertainty could be expressed as the variance of the prediction by using Monte Carlo Dropout Sampling (MC Dropout). Although MC Dropout has often been applied to fully connected layers in a network in recent studies, many convolutional layers are used to recognize the subtle features of a crater in the crater-detecting network. In this paper, we extended the application of MC Dropout to a network having a number of convolutional layers, and also evaluated the methodology of dropping out the convolutional layers. As a result, in the convolutional neural network, we represent the more correct variance by using filter-based dropout and evaluating the uncertainty for each feature map size. The precision of prediction in lunar crater detection was improved by 2.1% by rejecting a prediction result with high variance as a false positive compared with the variance when predicting the training data.
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References
Feng, D., Rosenbaum, L., Dietmayer, K.: Towards safe autonomous driving: capture uncertainty in the deep neural network for lidar 3D vehicle detection. In: 2018 21st International Conference on Intelligent Transportation Systems (ITSC) (2018). https://doi.org/10.1109/itsc.2018.8569814
Gal, Y., Ghahramani, Z.: Bayesian convolutional neural networks with Bernoulli approximate variational inference. In: 4th International Conference on Learning Representations (ICLR) Workshop Track (2016)
Gal, Y., Ghahramani, Z.: Dropout as a Bayesian approximation: representing model uncertainty in deep learning. In: Proceedings of the 33rd International Conference on International Conference on Machine Learning, ICML 2016, vol. 48, pp. 1050–1059. JMLR.org (2016)
Hashimoto, S., Mori, K.: Lunar crater detection based on grid partition using deep learning. In: 2019 IEEE 13th International Symposium on Applied Computational Intelligence and Informatics (SACI) (2019, to appear)
Liu, W., et al.: SSD: single shot MultiBox detector. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 21–37. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46448-0_2
Miller, D., Dayoub, F., Milford, M., Sünderhauf, N.: Evaluating merging strategies for sampling-based uncertainty techniques in object detection (2018). arXiv:1809.06006 [cs.CV]
Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 779–788 (2016). https://doi.org/10.1109/CVPR.2016.91
Redmon, J., Farhadi, A.: Yolov3: an incremental improvement (2018). arXiv:1804.02767 [cs.CV]
Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1137–1149 (2017). https://doi.org/10.1109/TPAMI.2016.2577031
Wang, H., Jiang, J., Zhang, G.: CraterIDNet: an end-to-end fully convolutional neural network for crater detection and identification in remotely sensed planetary images. Remote Sens. 10(7), 1067 (2018). https://doi.org/10.3390/rs10071067
Wetzler, P., Honda, R., Enke, B., Merline, W., Chapman, C., Burl, M.: Learning to detect small impact craters. In: 2005 Seventh IEEE Workshops on Applications of Computer Vision (WACV/MOTION 2005), vol. 1. IEEE (2005). https://doi.org/10.1109/acvmot.2005.68
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Myojin, T., Hashimoto, S., Mori, K., Sugawara, K., Ishihama, N. (2019). Improving Reliability of Object Detection for Lunar Craters Using Monte Carlo Dropout. In: Tetko, I., Kůrková, V., Karpov, P., Theis, F. (eds) Artificial Neural Networks and Machine Learning – ICANN 2019: Image Processing. ICANN 2019. Lecture Notes in Computer Science(), vol 11729. Springer, Cham. https://doi.org/10.1007/978-3-030-30508-6_6
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DOI: https://doi.org/10.1007/978-3-030-30508-6_6
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