Color-, depth-, and shape-based 3D fruit detection

Lin, Guimin; Tang, Yunchao; Zou, Xiangjun; Xiong, Juntao; Fang, Yamei

doi:10.1007/s11119-019-09654-w

Cited by 133 publications

(62 citation statements)

References 33 publications

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“…Probabilistic Completeness: for a robust feasible path planning problem (S free , S init , S goal ), when the number of iterations k approaches infinity, the distance between p a and (1) if k mod 2 � 0 then (2) if (x prev2 < x rand ) then (3) if (y prev2 < y rand ) then (4) if (z prev2 > z rand ) then (5) x bia � x rand − rand * x rand (6) y bia � y rand − rand * y rand (7) z bia � z rand + rand * (z prev2 − z rand ) (8) else (9) if (x prevl < x rand ) then (10) if (y prevl < y rand ) then (11) if (z prev1 > z rand ) then (12) x bia � x rand − rand * x rand (13) y bia � x rand − rand * y rand (14) z bia � z rand − rand * (z prev1 − z rand ) (15) return q bia � (x bia , y bia ,z bia ) (16) end ALGORITHM 5: Bi-bias(q rand , k, q prev1 , q prev2 ).…”

Section: Discussionmentioning

confidence: 99%

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Biased Sampling Potentially Guided Intelligent Bidirectional RRT^∗ Algorithm for UAV Path Planning in 3D Environment

Zhen

et al. 2019

Mathematical Problems in Engineering

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During the last decade, Rapidly-exploring Random Tree star (RRT∗) algorithm based on sampling has been widely used in the field of unmanned aerial vehicle (UAV) path planning for its probabilistically complete and asymptotically optimal characteristics. However, the convergence rate of RRT∗ as well as B-RRT∗ and IB-RRT∗ is slow for these algorithms perform pure exploration. To overcome the weaknesses above, Biased Sampling Potentially Guided Intelligent Bidirectional RRT∗ (BPIB-RRT∗) algorithm is proposed in this paper, which combines the bidirectional artificial potential field method with the idea of bidirectional biased sampling. The proposed algorithm flexibly adjusts the sampling space, greatly reduces the invalid spatial sampling, and improves the convergence rate. Moreover, the deeply theoretical analysis of the proposed BPIB-RRT∗ algorithm is given regarding its probabilistic completeness, asymptotic optimality, and computational complexity. Finally, compared to the latest UAV path planning algorithms, simulation comparisons are demonstrated to show the superiority of our proposed BPIB-RRT∗ algorithm.

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Section: Discussionmentioning

confidence: 99%

“…Nowadays, Unmanned Aerial Vehicles (UAV) are being widely applied to military or civilian fields, e.g., mining surveillance [1], search and rescue operations [2], image recognition [3], cropland assessment [4], and so on. e increasing demands have brought in focus on reducing dependencies on human operators.…”

Section: Introductionmentioning

confidence: 99%

Biased Sampling Potentially Guided Intelligent Bidirectional RRT^∗ Algorithm for UAV Path Planning in 3D Environment

Zhen

et al. 2019

Mathematical Problems in Engineering

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“…Nguyen et al (2016) applied descriptor of colour and geometry features to describe the appearance of red apples, and a clustering algorithm based on Euclidean distance in feature space to segment and detect the fruits in the images. The similar processing techniques of performing segmentation and detection in vision sensing in orchard environment are also presented in the works of (Zhou et al, 2012, Lin et al, 2019a. Wang and Xu (2018) applied multiple image features and Latent Dirichlet Allocation (LDA) model to perform unsupervised segmentation of the plant and fruits in the greenhouse environment, showing that traditional machine learning based algorithm can be used in generating labelling data for deep learning based algorithms.…”

Section: Literature Reviewmentioning

confidence: 99%

Fruit detection, segmentation and 3D visualisation of environments in apple orchards

Kang

Chen

2020

Computers and Electronics in Agriculture

164

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Robotic harvesting of fruits in orchards is a challenging task, since high density and overlapping of fruits and branches can heavily impact the success rate of robotic harvesting. Therefore, the vision system is demanded to provide comprehensive information of the working environment to guide the manipulator and gripping system to successful detach the target fruits. In this study, a deep learning based one-stage detector DaSNet-V2 is developed to perform the multi-task vision sensing in the working environment of apple orchards. DaSNet-V2 combines the detection and instance segmentation of fruits and semantic segmentation of branch into a single network architecture. Meanwhile, a light-weight backbone network LW-net is utilised in the DaSNet-V2 model to improve the computational efficiency of the model. In the experiment, DaSNet-V2 is tested and evaluated on the RGB-D images of the orchard. From the experiment results, DaSNet-V2 with lightweight backbone achieves 0.844, 0.858, and 0.795 on the F 1 score of the detection, and mean intersection of union on the instance segmentation of fruits and semantic segmentation of branches, respectively. To provide a direct-viewing of the working environment in orchards, the obtained sensing results are illustrated by 3D visualisation . The robustness and efficiency of the DaSNet-V2 in detection and segmentation are validated by the experiments in the real-environment of apple orchard.

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“…Specific studies in the agri-food sector and the related behavior of neural networks have been considered. It is worth mentioning the studies conducted by Guichao et al [7][8][9] on detection systems for the artificial visualization of fruits through RGB space color analysis and robotic systems. In the case of the classification of apples, Yang et al [10] provide positive results.…”

Section: Introductionmentioning

confidence: 99%

Analysis of the Functionality of the Feed Chain in Olive Pitting, Slicing and Stuffing Machines by IoT, Computer Vision and Neural Network Diagnosis

Pascual

Luna

Lázaro

et al. 2020

Sensors

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Olive pitting, slicing and stuffing machines (DRR in Spanish) are characterized by the fact that their optimal functioning is based on appropriate adjustments. Traditional systems are not completely reliable because their minimum error rate is 1–2%, which can result in fruit loss, since the pitting process is not infallible, and food safety issues can arise. Such minimum errors are impossible to remove through mechanical adjustments. In order to achieve this objective, an innovative solution must be provided in order to remove errors at operating speed rates over 2500 olives/min. This work analyzes the appropriate placement of olives in the pockets of the feed chain by using the following items: (1) An IoT System to control the DRR machine and the data analysis. (2) A computer vision system with an external shot camera and a LED lighting system, which takes a picture of every pocket passing in front of the camera. (3) A chip with a neural network for classification that, once trained, classifies between four possible pocket cases: empty, normal, incorrectly de-stoned olives at any angles (also known as a “boat”), and an anomalous case (foreign elements such as leafs, small branches or stones, two olives or small parts of olives in the same pocket). The main objective of this paper is to illustrate how with the use of a system based on IoT and a physical chip (NeuroMem CM1K, General Vision Inc.) with neural networks for sorting purposes, it is possible to optimize the functionality of this type of machine by remotely analyzing the data obtained. The use of classifying hardware allows it to work at the nominal operating speed for these machines. This would be limited if other classifying techniques based on software were used.

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Color-, depth-, and shape-based 3D fruit detection

Cited by 133 publications

References 33 publications

Biased Sampling Potentially Guided Intelligent Bidirectional RRT^∗ Algorithm for UAV Path Planning in 3D Environment

Biased Sampling Potentially Guided Intelligent Bidirectional RRT^∗ Algorithm for UAV Path Planning in 3D Environment

Fruit detection, segmentation and 3D visualisation of environments in apple orchards

Analysis of the Functionality of the Feed Chain in Olive Pitting, Slicing and Stuffing Machines by IoT, Computer Vision and Neural Network Diagnosis

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Color-, depth-, and shape-based 3D fruit detection

Cited by 133 publications

References 33 publications

Biased Sampling Potentially Guided Intelligent Bidirectional RRT∗ Algorithm for UAV Path Planning in 3D Environment

Biased Sampling Potentially Guided Intelligent Bidirectional RRT∗ Algorithm for UAV Path Planning in 3D Environment

Fruit detection, segmentation and 3D visualisation of environments in apple orchards

Analysis of the Functionality of the Feed Chain in Olive Pitting, Slicing and Stuffing Machines by IoT, Computer Vision and Neural Network Diagnosis

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Biased Sampling Potentially Guided Intelligent Bidirectional RRT^∗ Algorithm for UAV Path Planning in 3D Environment

Biased Sampling Potentially Guided Intelligent Bidirectional RRT^∗ Algorithm for UAV Path Planning in 3D Environment