3D-Vision Based Detection, Localisation and Sizing of Broccoli Heads in the Field

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F17%3A00311498" target="_blank" >RIV/68407700:21230/17:00311498 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1002/rob.21726" target="_blank" >http://dx.doi.org/10.1002/rob.21726</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1002/rob.21726" target="_blank" >10.1002/rob.21726</a>

Jazyk výsledku

angličtina

Název v původním jazyce

3D-Vision Based Detection, Localisation and Sizing of Broccoli Heads in the Field

Popis výsledku v původním jazyce

This paper describes a 3D vision system for robotic harvesting of broccoli using low-cost RGB-D sensors, which was developed and evaluated using sensory data collected under real-world field conditions in both the UK and Spain. The presented method addresses the tasks of detecting mature broccoli heads in the field and providing their 3D locations relative to the vehicle. The paper evaluates different 3D features, machine learning and temporal filtering methods for detection of broccoli heads. Our experiments show that a combination of Viewpoint Feature Histograms, Support Vector Machine classifier and a temporal filter to track the detected heads results in a system that detects broccoli heads with high precision. We also show that the temporal filtering can be used to generate a 3D map of the broccoli head positions in the field. Additionally we present methods for automatically estimating the size of the broccoli heads, to determine when a head is ready for harvest. All of the methods were evaluated using ground-truth data from both the UK and Spain, which we also make available to the research community for subsequent algorithm development and result comparison. Cross-validation of the system trained on the UK dataset on the Spanish dataset, and vice versa, indicated good generalisation capabilities of the system, confirming the strong potential of low-cost 3D imaging for commercial broccoli harvesting.

Název v anglickém jazyce

3D-Vision Based Detection, Localisation and Sizing of Broccoli Heads in the Field

Popis výsledku anglicky

This paper describes a 3D vision system for robotic harvesting of broccoli using low-cost RGB-D sensors, which was developed and evaluated using sensory data collected under real-world field conditions in both the UK and Spain. The presented method addresses the tasks of detecting mature broccoli heads in the field and providing their 3D locations relative to the vehicle. The paper evaluates different 3D features, machine learning and temporal filtering methods for detection of broccoli heads. Our experiments show that a combination of Viewpoint Feature Histograms, Support Vector Machine classifier and a temporal filter to track the detected heads results in a system that detects broccoli heads with high precision. We also show that the temporal filtering can be used to generate a 3D map of the broccoli head positions in the field. Additionally we present methods for automatically estimating the size of the broccoli heads, to determine when a head is ready for harvest. All of the methods were evaluated using ground-truth data from both the UK and Spain, which we also make available to the research community for subsequent algorithm development and result comparison. Cross-validation of the system trained on the UK dataset on the Spanish dataset, and vice versa, indicated good generalisation capabilities of the system, confirming the strong potential of low-cost 3D imaging for commercial broccoli harvesting.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/GJ17-27006Y" target="_blank" >GJ17-27006Y: Prostorově temporální representace pro dlouhodobou navigaci mobilních robotů</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2017

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Journal of Field Robotics

ISSN

1556-4959

e-ISSN

1556-4967

Svazek periodika

34

Číslo periodika v rámci svazku

8

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

14

Strana od-do

1505-1518

Kód UT WoS článku

000414680500007

EID výsledku v databázi Scopus

2-s2.0-85020072313