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

• URL: http://arxiv.org/abs/2308.01389v1
• Date: Wed, 2 Aug 2023 19:08:57 GMT
• Title: Follow the Soldiers with Optimized Single-Shot Multibox Detection and Reinforcement Learning
• Authors: Jumman Hossain, Maliha Momtaz
• Abstract summary: We 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. 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• VSSD: Vision Mamba with Non-Causal State Space Duality [26.96416515847115]
  State Space Models (SSMs) have gained prominence in vision tasks as they offer linear computational complexity. We introduce Visual State Space Duality (VSSD) model, which has a non-causal format of SSD. We conduct extensive experiments on various benchmarks including image classification, detection, and segmentation, where VSSD surpasses existing state-of-the-art SSM-based models.
  arXiv  Detail & Related papers  (2024-07-26T07:16:52Z)
• Exploring Dynamic Transformer for Efficient Object Tracking [58.120191254379854]
  We propose DyTrack, a dynamic transformer framework for efficient tracking. DyTrack automatically learns to configure proper reasoning routes for various inputs, gaining better utilization of the available computational budget. Experiments on multiple benchmarks demonstrate that DyTrack achieves promising speed-precision trade-offs with only a single model.
  arXiv  Detail & Related papers  (2024-03-26T12:31:58Z)
• Random resistive memory-based deep extreme point learning machine for unified visual processing [67.51600474104171]
  We propose a novel hardware-software co-design, random resistive memory-based deep extreme point learning machine (DEPLM) Our co-design system achieves huge energy efficiency improvements and training cost reduction when compared to conventional systems.
  arXiv  Detail & Related papers  (2023-12-14T09:46:16Z)
• Semantic Segmentation in Satellite Hyperspectral Imagery by Deep Learning [54.094272065609815]
  We propose a lightweight 1D-CNN model, 1D-Justo-LiuNet, which outperforms state-of-the-art models in the hypespectral domain. 1D-Justo-LiuNet achieves the highest accuracy (0.93) with the smallest model size (4,563 parameters) among all tested models.
  arXiv  Detail & Related papers  (2023-10-24T21:57:59Z)
• SSD-Faster Net: A Hybrid Network for Industrial Defect Inspection [0.7843067454030996]
  We propose a hybrid network, SSD-Faster Net, for industrial defect inspection of rails, insulators, commutators etc. SSD-Faster Net is a two-stage network, including SSD for quickly locating defective blocks, and an improved Faster R-CNN for defect segmentation. Experiments show that our SSD-Faster Net achieves an average accuracy of 84.03%, which is 13.42% higher than the nearest competitor.
  arXiv  Detail & Related papers  (2022-07-03T08:52:15Z)
• A Learning-based Approach Towards Automated Tuning of SSD Configurations [3.8975567119716805]
  We present an automated learning-based framework, named LearnedSSD, for tuning of hardware configurations for solid-state drives (SSDs) LearnedSSD automatically extracts the unique access patterns of a new workload using its block I/O traces, maps the workload to previously workloads for utilizing the learned experiences, and recommends an optimal SSD configuration based on the validated storage performance. We develop LearnedSSD with simple yet effective learning algorithms that can run efficiently on multi-core CPUs.
  arXiv  Detail & Related papers  (2021-10-17T00:25:21Z)
• Efficient and Robust LiDAR-Based End-to-End Navigation [132.52661670308606]
  We present an efficient and robust LiDAR-based end-to-end navigation framework. We propose Fast-LiDARNet that is based on sparse convolution kernel optimization and hardware-aware model design. We then propose Hybrid Evidential Fusion that directly estimates the uncertainty of the prediction from only a single forward pass.
  arXiv  Detail & Related papers  (2021-05-20T17:52:37Z)
• SE-SSD: Self-Ensembling Single-Stage Object Detector From Point Cloud [44.009023567586446]
  We present Self-Ensembling Single-Stage object Detector (SE-SSD) for accurate and efficient 3D object detection in point clouds. Our key focus is on exploiting both soft and hard targets with our formulated constraints. Our SE-SSD attains top performance compared with all prior published works.
  arXiv  Detail & Related papers  (2021-04-20T07:33:03Z)
• DMD: A Large-Scale Multi-Modal Driver Monitoring Dataset for Attention and Alertness Analysis [54.198237164152786]
  Vision is the richest and most cost-effective technology for Driver Monitoring Systems (DMS) The lack of sufficiently large and comprehensive datasets is currently a bottleneck for the progress of DMS development. In this paper, we introduce the Driver Monitoring dataset (DMD), an extensive dataset which includes real and simulated driving scenarios.
  arXiv  Detail & Related papers  (2020-08-27T12:33:54Z)
• FSSD: Feature Fusion Single Shot Multibox Detector [8.016875965887815]
  FSSD (Feature Fusion Single Shot Multibox Detector) is an enhanced SSD with a novel and lightweight feature fusion module. Our network can achieve 82.7 mAP (mean average precision) at the speed of 65.8 FPS (frame per second) with the input size 300$times$300 using a single Nvidia 1080Ti GPU.
  arXiv  Detail & Related papers  (2017-12-04T09:05:55Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.