Situation Awareness of Mars helicopter based on YOLOv7

Abstract

Abstract Situation awareness on Mars is indispensable for various downstream applications such as navigation and path planning, mapping and scientific discover. However, current Mars rover platform suffered from the absence of global information, leading to mission reduction and the failure of global planning. Compared to the rover, Mars helicopter is a new type of attempt for Mars exploration, which can provide a wide range view of scenes to well support the above missions. To this end, in this paper we proposed a novel learning-based situation awareness model for Mars helicopter. We modified the Yolov7 model by embedding a new channel attention on its head to improve the accuracy of detection. Besides, we designed a Tiny-Scale Detection Module (TSDM) to capture small or hidden rocks in complex Martian surface. To achieve further refinement and validate our proposal, we built an open-source synthetic dataset in air view. After extensive experiments, our model achieved excellent outperforms than the baseline model in our Mars dataset.