scholarly journals Vision-Based Moving Obstacle Detection and Tracking in Paddy Field Using Improved Yolov3 and Deep SORT

Sensors ◽  
2020 ◽  
Vol 20 (15) ◽  
pp. 4082 ◽  
Author(s):  
Zhengjun Qiu ◽  
Nan Zhao ◽  
Lei Zhou ◽  
Mengcen Wang ◽  
Liangliang Yang ◽  
...  
Keyword(s):  
Obstacle Avoidance ◽  
Processing Speed ◽  
Paddy Field ◽  
Autonomous Navigation ◽  
Vision System ◽  
Obstacle Detection ◽  
Paddy Fields ◽  
Future Research ◽  
Moving Obstacles ◽  
Agricultural Machines

Using intelligent agricultural machines in paddy fields has received great attention. An obstacle avoidance system is required with the development of agricultural machines. In order to make the machines more intelligent, detecting and tracking obstacles, especially the moving obstacles in paddy fields, is the basis of obstacle avoidance. To achieve this goal, a red, green and blue (RGB) camera and a computer were used to build a machine vision system, mounted on a transplanter. A method that combined the improved You Only Look Once version 3 (Yolov3) and deep Simple Online and Realtime Tracking (deep SORT) was used to detect and track typical moving obstacles, and figure out the center point positions of the obstacles in paddy fields. The improved Yolov3 has 23 residual blocks and upsamples only once, and has new loss calculation functions. Results showed that the improved Yolov3 obtained mean intersection over union (mIoU) score of 0.779 and was 27.3% faster in processing speed than standard Yolov3 on a self-created test dataset of moving obstacles (human and water buffalo) in paddy fields. An acceptable performance for detecting and tracking could be obtained in a real paddy field test with an average processing speed of 5–7 frames per second (FPS), which satisfies actual work demands. In future research, the proposed system could support the intelligent agriculture machines more flexible in autonomous navigation.

Obstacle Detection and Avoidance Using Wavefront Planner and Kinect on a WMRA

2012 ◽  
Author(s):  
Ivan Shindev ◽  
Shane Marlin ◽  
Nathan Preseault ◽  
Rodrigo Tamayo ◽  
William Pence ◽  
...  
Keyword(s):  
Obstacle Avoidance ◽  
Autonomous Navigation ◽  
Obstacle Detection ◽  
Robotic Arm ◽  
Accurate Representation ◽  
Start Position ◽  
Autonomous Operation ◽  
Suitable Solution ◽  
Navigation Control ◽  
Wheelchair Mounted Robotic Arm

Obstacle avoidance in autonomous navigation platforms is a well known problem that can be solved in numerous ways. This paper considers and analyzes the use of wavefront planner as an obstacle avoidance algorithm for a 9-DoF wheelchair-mounted robotic arm (WMRA) [1]. It also presents a suitable solution for obstacle detection using the OpenNI driver for interfacing with Microsoft’s Kinect. It further analyzes the capabilities of an autonomous operation of the WMRA and explains how this algorithm can be implemented into its navigation control. The results of this project showed that the Kinect can provide a very accurate representation of the surroundings. The wavefront planner can use this data to find a path from a start position to a goal without running into an obstacle.

scholarly journals UAV Obstacle Avoidance Algorithm to Navigate in Dynamic Building Environments

Drones ◽  
2022 ◽  
Vol 6 (1) ◽  
pp. 16
Author(s):  
Enrique Aldao ◽  
Luis M. González-deSantos ◽  
Humberto Michinel ◽  
Higinio González-Jorge
Keyword(s):  
Real Time ◽  
Collision Avoidance ◽  
Obstacle Avoidance ◽  
Autonomous Navigation ◽  
Point Cloud ◽  
Progress Monitoring ◽  
Flight Trajectory ◽  
Moving Obstacles ◽  
Current Implementation ◽  
Construction Progress Monitoring

In this work, a real-time collision avoidance algorithm was presented for autonomous navigation in the presence of fixed and moving obstacles in building environments. The current implementation is designed for autonomous navigation between waypoints of a predefined flight trajectory that would be performed by an UAV during tasks such as inspections or construction progress monitoring. It uses a simplified geometry generated from a point cloud of the scenario. In addition, it also employs information from 3D sensors to detect and position obstacles such as people or other UAVs, which are not registered in the original cloud. If an obstacle is detected, the algorithm estimates its motion and computes an evasion path considering the geometry of the environment. The method has been successfully tested in different scenarios, offering robust results in all avoidance maneuvers. Execution times were measured, demonstrating that the algorithm is computationally feasible to be implemented onboard an UAV.

scholarly journals Autonomous navigation system with small four-wheel drive platform

2020 ◽  
Vol 166 ◽  
pp. 05004
Author(s):  
Martin Bogdanovskyi ◽  
Andrii Tkachuk ◽  
Oleksandr Dobrzhanskyi ◽  
Anna Humeniuk
Keyword(s):  
Autonomous Navigation ◽  
Hybrid Control ◽  
Vision System ◽  
Movement Control ◽  
Image Sensor ◽  
Obstacle Detection ◽  
Raspberry Pi ◽  
Navigation Systems ◽  
Wheel Drive ◽  
Four Wheel Drive

The task of achieving greater flexibility and maneuverability of small transport and service units’ motion in modern factories by developing small autonomous navigation systems plays crucial role in complex automation of transport logistics nowadays. To solve navigation task, it was proposed the following approach, where as a means of assessing the environment was used computer vision system based on 5-megapixel CMOS image sensor and for the front obstacle detection was used auxiliary ultrasonic sensor as a limit switch. Authors solved the problem of yawing using artificial marking approach as along two-colored leading lines. For maneuverability increase during the turn was used speed movement control based on lines perspective. The basic design and technical characteristics of the four-wheel drive platform and the algorithm of the Raspberry PI 3/Arduino Nano hybrid control system are presented. Experimental results proved the viability of the proposed approach.

Obstacle Avoidance and Navigation of Autonomous Mobile Robot

2011 ◽  
Vol 403-408 ◽  
pp. 4633-4642 ◽  
Author(s):  
Rekha Raja ◽  
S N. Shome ◽  
S. Nandy ◽  
R. Ray
Keyword(s):  
Mobile Robot ◽  
Obstacle Avoidance ◽  
Autonomous Navigation ◽  
Fuzzy Logic Controller ◽  
Classical Method ◽  
Obstacle Detection ◽  
Autonomous Mobile Robot ◽  
Unstructured Environment ◽  
Multiple Obstacles ◽  
Robot Platform

This paper presents a hybrid obstacle avoidance methodology for autonomous navigation of a mobile robot in an unstructured environment. Decision is taken based on the classical method depending on the environmental scenario where the space between multiple obstacles is measured and the feasibility of passing the robot through any immediate pair of obstacles examined. In other cases, the decision is taken by the Fuzzy Logic controller. The developed algorithm is simulated and experimentally validated with a mobile robot platform equipped with forward-looking sonar for obstacle detection. Odometry sensors assist in localization of the mobile robot. The developed algorithm is found adequately intelligent to navigate the robot from any start position through to the desired goal position avoiding obstacles, and without taking recourse to any pre-built map. The simulated results exhibit fair agreement with the experimental results.

Autonomous Navigation and Obstacle Avoidance

2009 ◽  
Author(s):  
Jesse Berger ◽  
Cory Carson ◽  
Massood Towhidnejad ◽  
Richard Stansbury
Keyword(s):  
Obstacle Avoidance ◽  
Autonomous Navigation

scholarly journals A State-of-the-Art Analysis of Obstacle Avoidance Methods from the Perspective of an Agricultural Sprayer UAV’s Operation Scenario

Agronomy ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1069
Author(s):  
Shibbir Ahmed ◽  
Baijing Qiu ◽  
Fiaz Ahmad ◽  
Chun-Wei Kong ◽  
Huang Xin
Keyword(s):  
Collision Avoidance ◽  
Obstacle Avoidance ◽  
State Of The Art ◽  
Obstacle Detection ◽  
Crop Monitoring ◽  
Data Intensive ◽  
Structural Details ◽  
Dynamic Operating ◽  
New Generation ◽  
The Internet Of Things

Over the last decade, Unmanned Aerial Vehicles (UAVs), also known as drones, have been broadly utilized in various agricultural fields, such as crop management, crop monitoring, seed sowing, and pesticide spraying. Nonetheless, autonomy is still a crucial limitation faced by the Internet of Things (IoT) UAV systems, especially when used as sprayer UAVs, where data needs to be captured and preprocessed for robust real-time obstacle detection and collision avoidance. Moreover, because of the objective and operational difference between general UAVs and sprayer UAVs, not every obstacle detection and collision avoidance method will be sufficient for sprayer UAVs. In this regard, this article seeks to review the most relevant developments on all correlated branches of the obstacle avoidance scenarios for agricultural sprayer UAVs, including a UAV sprayer’s structural details. Furthermore, the most relevant open challenges for current UAV sprayer solutions are enumerated, thus paving the way for future researchers to define a roadmap for devising new-generation, affordable autonomous sprayer UAV solutions. Agricultural UAV sprayers require data-intensive algorithms for the processing of the images acquired, and expertise in the field of autonomous flight is usually needed. The present study concludes that UAV sprayers are still facing obstacle detection challenges due to their dynamic operating and loading conditions.

scholarly journals The Value of a Decrease in Temperature by One Degree Celsius of the Regional Microclimate—The Cooling Effect of the Paddy Field

Atmosphere ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 353
Author(s):  
Ya-Wen Chiueh ◽  
Chih-Hung Tan ◽  
Hsiang-Yi Hsu
Keyword(s):  
Land Use ◽  
Surface Temperature ◽  
Paddy Field ◽  
Research Area ◽  
Paddy Fields ◽  
Cooling Effect ◽  
Field Temperature ◽  
Bare Land ◽  
The Face ◽  
The Difference

In the face of climate change, extreme climates are becoming more frequent. There were severe droughts in Taiwan in 2020, 2014–2015, and 2002. In these years, the paddy fields were kept fallow to save water and transfer agricultural water to non-agricultural use. On the other hand, with global warming, the existence of paddy fields may be one of the natural solutions to regional temperature mitigation. This study used remote sensing to quantify the difference in temperature between paddy fields and urban areas. The result of overall surface temperature deductive analysis revealed that the temperature in the whole Taoyuan research area was 1.2 °C higher in 2002 than in 2003 because of fallowing of the paddy field, while in the Hsinchu research area, it was 1.5 °C higher in 2002 than in 2003, due to the same reason described above. In terms of the difference in land use, for the Hsinchu research area, the surface temperature deductive result showed that the average paddy field temperature in 2002 was 22.3 °C (sample area average), which was 7.7 °C lower than that of the building and road point and 4.3 °C lower than that of the bare land point. The average paddy field temperature in 2003 was 19.2 °C (sample area average), which was 10.1 °C lower than that of the building and road point and 8.3 °C lower than that of the bare land point. Then this study evaluated the economic valuation of the paddy field cooling effect using the contingent valuation method. Through the paddy field cooling effect and in the face of worsening extreme global climate, the willingness to pay (WTP) of the respondents in Taiwan for a decrease of 1 °C with regard to the regional microclimate was evaluated. It was found that people in Taiwan are willing to pay an extra 8.89 USD/per kg rice/year for the paddy for a decrease in temperature by 1 °C in the regional microclimate due to the paddy field. Furthermore, this study applied the benefits transfer method to evaluate the value of a decrease of 1 °C in the regional microclimate in Taiwan. The value of a decrease of 1 °C in the regional microclimate in Taiwan is 9,693,144,279 USD/year. In this regard, the economic value of 1 °C must not be underestimated. In conclusion, more caution is needed while making decisions to change the land use of paddy fields to other land uses.

scholarly journals Effects of Contemporary Land Use Types and Conversions from Wetland to Paddy Field or Dry Land on Soil Organic Carbon Fractions

2020 ◽  
Vol 12 (5) ◽  
pp. 2094
Author(s):  
Di Zhao ◽  
Junyu Dong ◽  
Shuping Ji ◽  
Miansong Huang ◽  
Quan Quan ◽  
...  
Keyword(s):  
Land Use ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Constructed Wetland ◽  
Paddy Field ◽  
Heavy Fraction ◽  
Light Fraction ◽  
Paddy Fields ◽  
Dry Land ◽  
Natural Wetlands

Soil organic carbon (SOC) concentration is closely related to soil quality and climate change. The objectives of this study were to estimate the effects of contemporary land use on SOC concentrations at 0–20 cm depths, and to investigate the dynamics of SOC in paddy-field soil and dry-land soil after their conversion from natural wetlands (20 and 30 years ago). We investigated the dissolved organic carbon (DOC), light fraction organic carbon (LFOC), heavy fraction organic carbon (HFOC), and other soil properties (i.e., moisture content, bulk density, pH, clay, sand, silt, available phosphorous, light fraction nitrogen, and heavy fraction nitrogen) in natural wetlands, constructed wetlands, fishponds, paddy fields, and soybean fields. The results indicated that the content of DOC increased 17% in constructed wetland and decreased 39% in fishponds, and the content of HFOC in constructed wetland and fishponds increased 50% and 8%, respectively, compared with that in natural wetlands at 0–20 cm. After the conversion of a wetland, the content of HFOC increased 72% in the paddy fields and decreased 62% in the dry land, while the content of DOC and LFOC decreased in both types. In the paddy fields, LFOC and HFOC content in the topmost 0.2 m of the soil layer was significantly higher compared to the layer below (from 0.2 to 0.6 m), and there were no significant differences observed in the dry land. The findings suggest that the paddy fields can sequester organic carbon through the accumulation of HFOC. However, the HFOC content decreased 22% after 10 years of cultivation with the decrease of clay content, indicating that paddy fields need to favor clay accumulation for the purpose of enhancing carbon sequestration in the paddy fields.

Recent Reviews on Machine Vision-Based 3D Reconstruction

2021 ◽  
Vol 14 ◽  
Author(s):  
Hongxin Zhang ◽  
Shaowei Ma ◽  
Meng Li ◽  
Hanghang Jiang ◽  
Jiaming Li
Keyword(s):  
Machine Vision ◽  
3D Reconstruction ◽  
Autonomous Navigation ◽  
Robot Vision ◽  
Research Direction ◽  
Development Trend ◽  
Future Research ◽  
The Future ◽  
The Development Trend ◽  
Better Than

Background: In machine vision, the 3D reconstruction is widely used in medical system, autonomous navigation, aviation and remote sensing measurement, industrial automation and other fields, and the demand for reconstruction precision is significantly highlighted. Therefore, the 3D reconstruction is of great research value and will be an important research direction in the future. Objective: By reviewing the latest development and patent of 3D reconstruction, this paper provides references to researchers in related fields. Methods: Machine vision-based 3D reconstruction patents and literatures were analyzed from the aspects of the algorithm, innovation and application. Among them, there are more than 30 patents and nearly 30 literatures in the past ten years. Results: Researches on machine vision-based 3D reconstruction in recent 10 years are reviewed, and the typical characteristics were concluded. The main problems in its development were analyzed, the development trend was foreseen, and the current and future research on the productions and patents related to machine vision-based 3D reconstruction is discussed. Conclusion: The reconstruction result of binocular vision and multi-vision is better than monocular vision in most cases. Current researches of 3D reconstruction focus on robot vision navigation, intelligent vehicle environment sensing system and virtual reality. The aspects that need to be improved in the future include: improving robustness, reducing computational complexity, and reducing operating equipment requirements, and so on. Furthermore, more patents on machine vision-based 3D reconstruction also should be invented.

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