Follow the Soldiers with Optimized Single-Shot Multibox Detection and Reinforcement Learning

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2308.01389.pdf (533.72 KB)

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https://arxiv.org/abs/2308.01389

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https://doi.org/10.48550/arXiv.2308.01389
 http://hdl.handle.net/11603/29308

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UMBC Information Systems Department
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2023-08-02

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journal articles
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Abstract

Nowadays, autonomous cars are gaining traction due to their numerous potential applications on battlefields and in resolving a variety of other real-world challenges. The main goal of our project is to build an autonomous system using DeepRacer which will follow a specific person (for our project, a soldier) when they will be moving in any direction. Two main components to accomplish this project is an optimized Single-Shot Multibox Detection (SSD) object detection model and a Reinforcement Learning (RL) model. We accomplished the task using SSD Lite instead of SSD and at the end, compared the results among SSD, SSD with Neural Computing Stick (NCS), and SSD Lite. Experimental results show that SSD Lite gives better performance among these three techniques and exhibits a considerable boost in inference speed (~2-3 times) without compromising accuracy.