Mask R-CNN: Detection Performance on SPEED Spacecraft With Image Degradation

Abstract

Convolutional neural networks in the task of object detection and localization have been evolving in the last few years. Various convolutional network models have been proposed such as Faster Region-Based, Mask Region-Based, Single Shot Detection, and "You Only Look Once" models (with different versions). Although instance segmentation has been explored with many models, the Mask Region-Based Convolutional Neural Network has been one of the most competitive models in terms of overall object detection performance. Its widespread use in many different applications encouraged us to take a closer look at model performance in a unique object detection task, namely the detection of spacecraft images in the wild. The main research question in this paper, is whether this off-the-shelf proposed architecture can effectively detect and localize a spacecraft when using the Spacecraft Pose Estimation Dataset, under a variety of different image degradation factors and at various degradation levels. The inspiration for this investigation is the effect of deep space environments on charge-coupled device image sensors, and other imaging hardware. The capability of detection and localization to continue in the face of pixel loss and Gaussian noise are explored. The effects of training augmentation on object detection performance is another task that has also been studied. Some of our main findings include that supplementing training on degraded images improve significantly the detection results. In low degradation scenarios, the improvement is better than the baseline results. Also, the proposed models is able to detect and localize properly on spacecraft images degraded by pixel loss. The proposed model continues to perform close to baseline in conditions even up to 80% pixel loss for the black background experiments.