An Intelligent Real-Time Object Detection System on Drones

An Intelligent Real-Time Object Detection System on Drones

Drones have been widely used in everyday life and they can help deal with various tasks, including photography, searching, and surveillance. Nonetheless, it is difficult for drones to perform customized online real-time object detection. In this study, we propose an intelligent real-time object dete...

Full description

Bibliographic Details
Main Authors:	Chao Chen, Hongrui Min, Yi Peng, Yongkui Yang, Zheng Wang
Format:	Article
Language:	English
Published:	MDPI AG 2022-10-01
Series:	Applied Sciences
Subjects:	neural network accelerator FPGA object detection intelligent system machine learning
Online Access:	https://www.mdpi.com/2076-3417/12/20/10227

Similar Items

Reduced-Parameter YOLO-like Object Detector Oriented to Resource-Constrained Platform
by: Xianbin Zheng, et al.
Published: (2023-03-01)

Adaptive Real-Time Object Detection for Autonomous Driving Systems
by: Maryam Hemmati, et al.
Published: (2022-04-01)

Resource- and Power-Efficient High-Performance Object Detection Inference Acceleration Using FPGA
by: Solomon Negussie Tesema, et al.
Published: (2022-06-01)

Resources and Power Efficient FPGA Accelerators for Real-Time Image Classification
by: Angelos Kyriakos, et al.
Published: (2022-04-01)

A Reconfigurable CNN-Based Accelerator Design for Fast and Energy-Efficient Object Detection System on Mobile FPGA
by: Victoria Heekyung Kim, et al.
Published: (2023-01-01)

Enhancing Embedded Object Tracking: A Hardware Acceleration Approach for Real-Time Predictability
by: Mingyang Zhang, et al.
Published: (2024-03-01)

Configurable Hardware Core for IoT Object Detection
by: Pedro R. Miranda, et al.
Published: (2021-10-01)

Accelerating Event Detection with DGCNN and FPGAs
by: Zhe Han, et al.
Published: (2020-10-01)

Edge Real-Time Object Detection and DPU-Based Hardware Implementation for Optical Remote Sensing Images
by: Chao Li, et al.
Published: (2023-08-01)

MobileNets Can Be Lossily Compressed: Neural Network Compression for Embedded Accelerators
by: Se-Min Lim, et al.
Published: (2022-03-01)

Hardware–Software Partitioning for Real-Time Object Detection Using Dynamic Parameter Optimization
by: Corneliu Zaharia, et al.
Published: (2023-05-01)

DroneNet: Rescue Drone-View Object Detection
by: Xiandong Wang, et al.
Published: (2023-07-01)

Realtime Malicious Traffic Detection Targeted for TCP Out-of-Order Packets Based on FPGA
by: Zhenguo Hu, et al.
Published: (2023-01-01)

Efficient Dynamic Reconfigurable CNN Accelerator for Edge Intelligence Computing on FPGA
by: Kaisheng Shi, et al.
Published: (2023-03-01)

Real-time fast learning hardware implementation
by: Zhang Ming Jun, et al.
Published: (2023-01-01)

A Spiking LSTM Accelerator for Automatic Speech Recognition Application Based on FPGA
by: Tingting Yin, et al.
Published: (2024-02-01)

Real-Time Moving Object Detection in High-Resolution Video Sensing
by: Haidi Zhu, et al.
Published: (2020-06-01)

Intelligent Monitoring for DC Motor Performance Based on FPGA
by: Abbas H. Issa, et al.
Published: (2016-12-01)

Active Object Detection and Tracking Using Gimbal Mechanisms for Autonomous Drone Applications
by: Jakob Grimm Hansen, et al.
Published: (2024-02-01)

Computational Intelligence-Based Harmony Search Algorithm for Real-Time Object Detection and Tracking in Video Surveillance Systems
by: Maged Faihan Alotaibi, et al.
Published: (2022-02-01)

The Architecture of a Real-Time Control System for Heating Energy Management in the Intelligent Building
by: Daniel Prusak, et al.
Published: (2021-08-01)

Real-Time LiDAR Point-Cloud Moving Object Segmentation for Autonomous Driving
by: Xing Xie, et al.
Published: (2023-01-01)

FPGA Implementation of a Deep Learning Acceleration Core Architecture for Image Target Detection
by: Xu Yang, et al.
Published: (2023-03-01)

Intelligent Security Monitoring System Based on RISC-V SoC
by: Wenjuan Wu, et al.
Published: (2021-06-01)

SIMPLE DRONE FOR OBJECT COLOR DETECTION
by: Malik Abdulrazzaq Jabbar
Published: (2017-11-01)

Lightweight and Energy-Efficient Deep Learning Accelerator for Real-Time Object Detection on Edge Devices
by: Kyungho Kim, et al.
Published: (2023-01-01)

A Low-Cost Fully Integer-Based CNN Accelerator on FPGA for Real-Time Traffic Sign Recognition
by: Jaemyung Kim, et al.
Published: (2022-01-01)

Research and Implementation of High Computational Power for Training and Inference of Convolutional Neural Networks
by: Tianling Li, et al.
Published: (2023-01-01)

Accelerating Neural Network Inference on FPGA-Based Platforms—A Survey
by: Ran Wu, et al.
Published: (2021-04-01)

A Parameterized Parallel Design Approach to Efficient Mapping of CNNs onto FPGA
by: Ning Mao, et al.
Published: (2023-02-01)

FPGA-Based Vehicle Detection and Tracking Accelerator
by: Jiaqi Zhai, et al.
Published: (2023-02-01)

Technology trends in deep learning networks for real-time object detection in drone environment
by: Jonghyeon Mun, et al.
Published: (2023-09-01)

Technology trends in deep learning networks for real-time object detection in drone environment
by: Jonghyeon Mun, et al.
Published: (2023-09-01)

Novel CNN-Based AP2D-Net Accelerator: An Area and Power Efficient Solution for Real-Time Applications on Mobile FPGA
by: Shuai Li, et al.
Published: (2020-05-01)

Intelligent and Real-Time Detection and Classification Algorithm for Recycled Materials Using Convolutional Neural Networks
by: Dimitris Ziouzios, et al.
Published: (2022-02-01)

Efficient YOLOv7-Drone: An Enhanced Object Detection Approach for Drone Aerial Imagery
by: Xiaofeng Fu, et al.
Published: (2023-10-01)

A Fast and Low-Power Detection System for the Missing Pin Chip Based on YOLOv4-Tiny Algorithm
by: Shiyi Chen, et al.
Published: (2023-04-01)

Improved YOLOv7 Algorithm for Small Object Detection in Unmanned Aerial Vehicle Image Scenarios
by: Xinmin Li, et al.
Published: (2024-02-01)

Deep Learning Techniques for Image Recognition and Object Detection
by: Sharada K., et al.
Published: (2023-01-01)

Real-Time Computer Vision for Tree Stem Detection and Tracking
by: Lucas A. Wells, et al.
Published: (2023-01-01)