Locating Objects Without Bounding Boxes

Ribera, Javier; Güera, David; Chen, Yuhao; Delp, Edward J.

doi:10.1109/cvpr.2019.00664

Cited by 91 publications

(59 citation statements)

References 59 publications

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“…over space or time), the performance of these algorithms is significantly degraded. Since deep learning (DL) models have already demonstrated excellent performance for imagebased classification in a wide range of applications, there is significant potential for their use in agriculturally focused analysis [3] such as leaf segmentation [4], leaf counting [5], plant counting [6]- [8] and yield prediction [9]. DL approaches are also conducive to model updating via transfer learning strategies.…”

Section: Introductionmentioning

confidence: 99%

Automatic Plant Counting and Location Based on a Few-Shot Learning Technique

Karami

Crawford

Delp

2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Plant counting and location are essential for both plant breeding experiments and production agriculture. Stand count indicates the overall emergence of plants compared to the number of seeds that were planted, while location provides information on the associated variability within a plot or geographic area of a field. Deep learning has been successfully applied in various application domains, including plant phenotyping. This paper proposes the use of deep learning techniques, more specifically, anchor-free detectors, to identify and count maize plants in RGB images acquired from Unmanned Aerial Vehicles (UAV). The results were obtained using a modified CenterNet architecture, with validation performed against manual human annotation. Experimental results demonstrated an overall precision >95% for examples where training and testing were performed on the same field. Few-shot learning was also explored, where the trained network was a) directly applied to the fields in other geographic areas and b) updated using small quantities of training data from the other locations.

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Section: Introductionmentioning

confidence: 99%

Automatic Plant Counting and Location Based on a Few-Shot Learning Technique

Karami

Crawford

Delp

2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…2011). Previous work in plant counting extended the analysis to different spatial resolutions (Li et al 2019) and alternative object localization approaches (Ribera et al 2019). The approach proposed was tested in two different image spatial resolutions.…”

Section: Discussionmentioning

confidence: 99%

Automated crop plant counting from very high-resolution aerial imagery

et al. 2020

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Knowing before harvesting how many plants have emerged and how they are growing is key in optimizing labour and efficient use of resources. Unmanned aerial vehicles (UAV) are a useful tool for fast and cost efficient data acquisition. However, imagery need to be converted into operational spatial products that can be further used by crop producers to have insight in the spatial distribution of the number of plants in the field. In this research, an automated method for counting plants from very high-resolution UAV imagery is addressed. The proposed method uses machine vision—Excess Green Index and Otsu’s method—and transfer learning using convolutional neural networks to identify and count plants. The integrated methods have been implemented to count 10 weeks old spinach plants in an experimental field with a surface area of 3.2 ha. Validation data of plant counts were available for 1/8 of the surface area. The results showed that the proposed methodology can count plants with an accuracy of 95% for a spatial resolution of 8 mm/pixel in an area up to 172 m2. Moreover, when the spatial resolution decreases with 50%, the maximum additional counting error achieved is 0.7%. Finally, a total amount of 170 000 plants in an area of 3.5 ha with an error of 42.5% was computed. The study shows that it is feasible to count individual plants using UAV-based off-the-shelf products and that via machine vision/learning algorithms it is possible to translate image data in non-expert practical information.

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“…The Hausdorff distance measures the distance between two sets of data [42], [43]. It is an important metric that is commonly used in many applications, for example, face recognition [44] and object locating [45]. Given two sets A and B, the Hausdorff distance between A and B involves the maximum-minimum (max-min) calculation…”

Section: A Hausdorff Distancementioning

confidence: 99%

A Group-Based Distance Learning Method for Semisupervised Fuzzy Clustering

Jing

Zheng

Shen

et al. 2022

IEEE Trans. Cybern.

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Learning a proper distance for clustering from prior knowledge falls into the realm of semisupervised fuzzy clustering. Although most existing learning methods take prior knowledge (e.g., pairwise constraints) into account, they pay little attention to local knowledge of data, which, however, can be utilized to optimize the distance. In this article, we propose a novel distance learning method, which learns from the Group-level information, for semisupervised fuzzing clustering. We first present a new format of constraint information, called Group-level constraints, by elevating the pairwise constraints (must-links and cannotlinks) from point level to Group level. The Groups, generated around data points contained in the pairwise constraints, carry not only the local information of data (the relation between close data points) but also more background information under some given limited prior knowledge. Then, we propose a novel method to learn a distance by using the Group-level constraints, namely, Group-based distance learning, in order to optimize the performance of fuzzy clustering. The distance learning process aims to pull must-link Groups as close as possible while pushing cannot-link Groups as far as possible. We formulate the learning process with the weights of constraints by invoking some linear and nonlinear transformations. The linear Group-based distance learning method is realized by means of semidefinite programming, and the nonlinear learning method is realized by Manuscript

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Locating Objects Without Bounding Boxes

Cited by 91 publications

References 59 publications

Automatic Plant Counting and Location Based on a Few-Shot Learning Technique

Automatic Plant Counting and Location Based on a Few-Shot Learning Technique

Automated crop plant counting from very high-resolution aerial imagery

A Group-Based Distance Learning Method for Semisupervised Fuzzy Clustering

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