scholarly journals Design of Citrus Fruit Detection System Based on Mobile Platform and Edge Computer Device

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 59
Author(s):  
Heqing Huang ◽  
Tongbin Huang ◽  
Zhen Li ◽  
Shilei Lyu ◽  
Tao Hong
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Target Detection ◽  
Detection System ◽  
Citrus Fruit ◽  
Detection Algorithm ◽  
Edge Computing ◽  
Detection Accuracy ◽  
Computing Device ◽  
Adaptive Fusion ◽  
Aerial Vehicle

Citrus fruit detection can provide technical support for fine management and yield determination of citrus orchards. Accurate detection of citrus fruits in mountain orchards is challenging because of leaf occlusion and citrus fruit mutual occlusion of different fruits. This paper presents a citrus detection task that combines UAV data collection, AI embedded device, and target detection algorithm. The system used a small unmanned aerial vehicle equipped with a camera to take full-scale pictures of citrus trees; at the same time, we extended the state-of-the-art model target detection algorithm, added the attention mechanism and adaptive fusion feature method, improved the model’s performance; to facilitate the deployment of the model, we used the pruning method to reduce the amount of model calculation and parameters. The improved target detection algorithm is ported to the edge computing end to detect the data collected by the unmanned aerial vehicle. The experiment was performed on the self-made citrus dataset, the detection accuracy was 93.32%, and the processing speed at the edge computing device was 180 ms/frame. This method is suitable for citrus detection tasks in the mountainous orchard environment, and it can help fruit growers to estimate their yield.

scholarly journals Parallel Ensemble Deep Learning for Real-Time Remote Sensing Video Multi-Target Detection

2021 ◽  
Vol 13 (21) ◽  
pp. 4377
Author(s):  
Long Sun ◽  
Jie Chen ◽  
Dazheng Feng ◽  
Mengdao Xing
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Unmanned Aerial Vehicle ◽  
Target Detection ◽  
Template Matching ◽  
Detection Algorithm ◽  
Detection Accuracy ◽  
Information Warfare ◽  
Aerial Vehicle ◽  
Uav Images

Unmanned aerial vehicle (UAV) is one of the main means of information warfare, such as in battlefield cruises, reconnaissance, and military strikes. Rapid detection and accurate recognition of key targets in UAV images are the basis of subsequent military tasks. The UAV image has characteristics of high resolution and small target size, and in practical application, the detection speed is often required to be fast. Existing algorithms are not able to achieve an effective trade-off between detection accuracy and speed. Therefore, this paper proposes a parallel ensemble deep learning framework for unmanned aerial vehicle video multi-target detection, which is a global and local joint detection strategy. It combines a deep learning target detection algorithm with template matching to make full use of image information. It also integrates multi-process and multi-threading mechanisms to speed up processing. Experiments show that the system has high detection accuracy for targets with focal lengths varying from one to ten times. At the same time, the real-time and stable display of detection results is realized by aiming at the moving UAV video image.

scholarly journals FPGA-Based Real-Time Moving Target Detection System for Unmanned Aerial Vehicle Application

2016 ◽  
Vol 2016 ◽  
pp. 1-16 ◽  
Author(s):  
Jia Wei Tang ◽  
Nasir Shaikh-Husin ◽  
Usman Ullah Sheikh ◽  
M. N. Marsono
Keyword(s):  
Embedded System ◽  
Real Time ◽  
Unmanned Aerial Vehicle ◽  
Target Detection ◽  
Detection System ◽  
Detection Algorithm ◽  
Moving Target ◽  
Moving Target Detection ◽  
Rescue Operation ◽  
Aerial Vehicle

Moving target detection is the most common task for Unmanned Aerial Vehicle (UAV) to find and track object of interest from a bird’s eye view in mobile aerial surveillance for civilian applications such as search and rescue operation. The complex detection algorithm can be implemented in a real-time embedded system using Field Programmable Gate Array (FPGA). This paper presents the development of real-time moving target detection System-on-Chip (SoC) using FPGA for deployment on a UAV. The detection algorithm utilizes area-based image registration technique which includes motion estimation and object segmentation processes. The moving target detection system has been prototyped on a low-cost Terasic DE2-115 board mounted with TRDB-D5M camera. The system consists of Nios II processor and stream-oriented dedicated hardware accelerators running at 100 MHz clock rate, achieving 30-frame per second processing speed for 640 × 480 pixels’ resolution greyscale videos.

scholarly journals Autonomous, Onboard Vision-Based Trash and Litter Detection in Low Altitude Aerial Images Collected by an Unmanned Aerial Vehicle

2021 ◽  
Vol 13 (5) ◽  
pp. 965
Author(s):  
Marek Kraft ◽  
Mateusz Piechocki ◽  
Bartosz Ptak ◽  
Krzysztof Walas
Keyword(s):  
Neural Networks ◽  
Unmanned Aerial Vehicle ◽  
Low Cost ◽  
Detection Algorithm ◽  
Aerial Images ◽  
Detection Accuracy ◽  
Deep Convolutional Neural Networks ◽  
Domain Specific ◽  
Aerial Vehicle ◽  
Low Altitude

Public littering and discarded trash are, despite the effort being put to limit it, still a serious ecological, aesthetic, and social problem. The problematic waste is usually localised and picked up by designated personnel, which is a tiresome, time-consuming task. This paper proposes a low-cost solution enabling the localisation of trash and litter objects in low altitude imagery collected by an unmanned aerial vehicle (UAV) during an autonomous patrol mission. The objects of interest are detected in the acquired images and put on the global map using a set of onboard sensors commonly found in typical UAV autopilots. The core object detection algorithm is based on deep, convolutional neural networks. Since the task is domain-specific, a dedicated dataset of images containing objects of interest was collected and annotated. The dataset is made publicly available, and its description is contained in the paper. The dataset was used to test a range of embedded devices enabling the deployment of deep neural networks for inference onboard the UAV. The results of measurements in terms of detection accuracy and processing speed are enclosed, and recommendations for the neural network model and hardware platform are given based on the obtained values. The complete system can be put together using inexpensive, off-the-shelf components, and perform autonomous localisation of discarded trash, relieving human personnel of this burdensome task, and enabling automated pickup planning.

scholarly journals A YOLO-Based Target Detection Model for Offshore Unmanned Aerial Vehicle Data

2021 ◽  
Vol 13 (23) ◽  
pp. 12980
Author(s):  
Zhenhua Wang ◽  
Xinyue Zhang ◽  
Jing Li ◽  
Kuifeng Luan
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Target Detection ◽  
Detection Accuracy ◽  
Suitable Model ◽  
Average Precision ◽  
Detection Model ◽  
Vehicle Data ◽  
Aerial Vehicle ◽  
Feature Pyramid ◽  
D Model

Target detection in offshore unmanned aerial vehicle data is still a challenge due to the complex characteristics of targets, such as multi-sizes, alterable orientation, and complex backgrounds. Herein, a YOLO-based detection model (YOLO-D) was proposed for target detection in offshore unmanned aerial vehicle data. Based on the YOLOv3 network, the residual module was improved by establishing dense connections and adding a dual-attention mechanism (CBAM) to enhance the use of features and global information. Then, the loss function of the YOLO-D model was added to the weight coefficients to increase detection accuracy for small-size targets. Finally, the feature pyramid network (FPN) was replaced by the secondary recursive feature pyramid network to reduce the impacts of a complicated environment. Taking the car, boat, and deposit near the coastline as the targets, the proposed YOLO-D model was compared against other models, including the faster R-CNN, SSD, YOLOv3, and YOLOv5, to evaluate its detection performance. The results showed that the evaluation metrics of the YOLO-D model, including precision (Pr), recall (Re), average precision (AP), and the mean of average precision (mAP), had the highest values. The mAP of the YOLO-D model increased by 37.95%, 39.44%, 28.46%, and 5.08% compared to the faster R-CNN, SSD, YOLOv3, and YOLOv5, respectively. The AP of the car, boat, and deposit reached 96.24%, 93.70%, and 96.79% respectively. Moreover, the YOLO-D model had a higher detection accuracy than other models, especially in the detection of small-size targets. Collectively, the proposed YOLO-D model is a suitable model for target detection in offshore unmanned aerial vehicle data.

scholarly journals Gait Phase Detection Based on Muscle Deformation with Static Standing-Based Calibration

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1081
Author(s):  
Tamon Miyake ◽  
Shintaro Yamamoto ◽  
Satoshi Hosono ◽  
Satoshi Funabashi ◽  
Zhengxue Cheng ◽  
...  
Keyword(s):  
Learning Algorithm ◽  
Detection System ◽  
Calibration Method ◽  
Detection Algorithm ◽  
Video Data ◽  
Detection Accuracy ◽  
Phase Detection ◽  
Gait Phase ◽  
Logistic Regression Algorithm ◽  
Short Time

Gait phase detection, which detects foot-contact and foot-off states during walking, is important for various applications, such as synchronous robotic assistance and health monitoring. Gait phase detection systems have been proposed with various wearable devices, sensing inertial, electromyography, or force myography information. In this paper, we present a novel gait phase detection system with static standing-based calibration using muscle deformation information. The gait phase detection algorithm can be calibrated within a short time using muscle deformation data by standing in several postures; it is not necessary to collect data while walking for calibration. A logistic regression algorithm is used as the machine learning algorithm, and the probability output is adjusted based on the angular velocity of the sensor. An experiment is performed with 10 subjects, and the detection accuracy of foot-contact and foot-off states is evaluated using video data for each subject. The median accuracy is approximately 90% during walking based on calibration for 60 s, which shows the feasibility of the static standing-based calibration method using muscle deformation information for foot-contact and foot-off state detection.

scholarly journals Research on Lightweight Infrared Pedestrian Detection Model Algorithm for Embedded Platform

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Zhaoli Wu ◽  
Xin Wang ◽  
Chao Chen
Keyword(s):  
Real Time ◽  
Target Detection ◽  
Pedestrian Detection ◽  
Infrared Image ◽  
Far Infrared ◽  
Detection Algorithm ◽  
Model Parameters ◽  
Detection Accuracy ◽  
Detection Model ◽  
Embedded Platform

Due to the limitation of energy consumption and power consumption, the embedded platform cannot meet the real-time requirements of the far-infrared image pedestrian detection algorithm. To solve this problem, this paper proposes a new real-time infrared pedestrian detection algorithm (RepVGG-YOLOv4, Rep-YOLO), which uses RepVGG to reconstruct the YOLOv4 backbone network, reduces the amount of model parameters and calculations, and improves the speed of target detection; using space spatial pyramid pooling (SPP) obtains different receptive field information to improve the accuracy of model detection; using the channel pruning compression method reduces redundant parameters, model size, and computational complexity. The experimental results show that compared with the YOLOv4 target detection algorithm, the Rep-YOLO algorithm reduces the model volume by 90%, the floating-point calculation is reduced by 93.4%, the reasoning speed is increased by 4 times, and the model detection accuracy after compression reaches 93.25%.

scholarly journals A Machine Learning Method for Vision-Based Unmanned Aerial Vehicle Systems to Understand Unknown Environments

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3245
Author(s):  
Tianyao Zhang ◽  
Xiaoguang Hu ◽  
Jin Xiao ◽  
Guofeng Zhang
Keyword(s):  
Machine Learning ◽  
Visual Servoing ◽  
Detection System ◽  
Detection Accuracy ◽  
Unknown Environment ◽  
Unknown Environments ◽  
Localization And Mapping ◽  
Computational Speed ◽  
Aerial Vehicle ◽  
High Level

What makes unmanned aerial vehicles (UAVs) intelligent is their capability of sensing and understanding new unknown environments. Some studies utilize computer vision algorithms like Visual Simultaneous Localization and Mapping (VSLAM) and Visual Odometry (VO) to sense the environment for pose estimation, obstacles avoidance and visual servoing. However, understanding the new environment (i.e., make the UAV recognize generic objects) is still an essential scientific problem that lacks a solution. Therefore, this paper takes a step to understand the items in an unknown environment. The aim of this research is to enable the UAV with basic understanding capability for a high-level UAV flock application in the future. Specially, firstly, the proposed understanding method combines machine learning and traditional algorithm to understand the unknown environment through RGB images; secondly, the You Only Look Once (YOLO) object detection system is integrated (based on TensorFlow) in a smartphone to perceive the position and category of 80 classes of objects in the images; thirdly, the method makes the UAV more intelligent and liberates the operator from labor; fourthly, detection accuracy and latency in working condition are quantitatively evaluated, and properties of generality (can be used in various platforms), transportability (easily deployed from one platform to another) and scalability (easily updated and maintained) for UAV flocks are qualitatively discussed. The experiments suggest that the method has enough accuracy to recognize various objects with high computational speed, and excellent properties of generality, transportability and scalability.

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