Abstract
Due to the complexity and danger of Mars’s environment, traditional Mars unmanned ground vehicles cannot efficiently perform Mars exploration missions. To solve this problem, the DeepLabV3+/Efficientnet hybrid network is proposed and applied to the scene area judgment for the Mars unmanned vehicle system. Firstly, DeepLabV3+ is used to extract the feature information of the Mars image due to its high accuracy. Then, the feature information is used as the input for Efficientnet, and the categories of scene areas are obtained, including safe area, report area, and dangerous area. Finally, according to three categories, the Mars unmanned vehicle system performs three operations: pass, report, and send. Experimental results show the effectiveness of the DeepLabV3+/Efficientnet hybrid network in the scene area judgment. Compared with the Efficientnet network, the accuracy of the DeepLabV3+/Efficientnet hybrid network is improved by approximately 18% and reaches 99.84%, which ensures the safety of the exploration mission for the Mars unmanned vehicle system.
Highlights
DescriptionThe Mars unmanned ground vehicle is unable to reach the designated position
Mars unmanned hicle system is composed of two parts: the Mars unmanned ground vehicle andvethe hicle system is composed of two The parts: theunmanned
Traditional convolutional neural networks generally expand the network by adjusting Traditional convolutional networks generally expand network channels, by adjustthe resolution of the input image,neural network depth, and the number of the convolution ing the resolution of the themodel input composite image, network the number of convolution while
Summary
The Mars unmanned ground vehicle is unable to reach the designated position. The Mars unmanned vehicle system is equipped. The Mars unmanned vehicle system is conceived. Mars unmanned hicle system is composed of two parts: the Mars unmanned ground vehicle andvethe hicle system is composed of two The parts: theunmanned. Marsschematic unmanned system is equipped with artificial intelligence of vehicle the Mars unmanned vehicle sysartificial intelligence algorithms. Mars unmanned ground vehicle encounters with artificial intelligence algorithms. Mars unmanned vehicle encounters stacles, cannot through them, failing to ground move forward
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