Improving Reliability of Object Detection for Lunar Craters Using Monte Carlo Dropout

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.