Automated Detection of Multitype Landforms on Mars Using a Light-Weight Deep Learning-Based Detector

Intification of geological salient landforms is a primary requirement for spacecraft motion estimation and obstacle avoidance. As a large volume of high-resolution images are acquired by the Mars reconnaissance orbiter, growing number of approaches are proposed to develop automated approaches to detect a particular landform on Mars. However, most existing objective detection models are limited to sliding window-based and morphology-based algorithms, which require complicated preprocessing operations and can hardly be generalized to detecting different types of landforms. In this article, we aimed at developing a multitype landform detection system based on a light-weight deep learning framework, which has a quite small model size but presents excellent performance. This specific deep learning-based framework is named as mini shot multibox detector (SSD), by downsizing and modifying the existing single SSD. In the mini-SSD, some components are further optimized to adapt to this domain specific problem. A pretraining strategy is well-designed and merged into the entire model training process. In the performance evaluation tests, the proposed framework was trained and tested on images with different scales collected from different locations in high resolution imaging science experiment database. Results demonstrate that the introduced Adam optimizer and pretraining strategy can form positive effective to both model training and inference performance. The proposed framework and strategy combination outperforms the original SSD300, faster R-CNN, and YOLO series models as well as all state-of-the-art sliding window-based detectors in the field, namely AdaBoost with LBP features, AdaBoost with Haar features, and support vector machines with histogram of oriented gradient (HOG) features, in two different testing sets. Additionally, it shows high resilience on detecting target landforms in different environments with various sizes and shapes from the qualitative analysis, and can be generalized as a tool for relevant applications.