Faster R-CNN With Classifier Fusion for Automatic Detection of Small Fruits

Mai, Xiaochun; Zhang, Hong; Xiao, Jia; Meng, Max Q.-H.

doi:10.1109/tase.2020.2964289

Cited by 46 publications

(23 citation statements)

References 39 publications

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“…Many studies have generally followed similar practices and used the region-based CNNs (e.g., RCNN and Faster RCNN) for plant/plant organ counting [85][86][87][88][89][90][91]. Two critical issues, however, were not addressed by these studies.…”

Section: Plant Phenomicsmentioning

confidence: 99%

Convolutional Neural Networks for Image-Based High-Throughput Plant Phenotyping: A Review

2020

Plant Phenomics

256

180

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Plant phenotyping has been recognized as a bottleneck for improving the efficiency of breeding programs, understanding plant-environment interactions, and managing agricultural systems. In the past five years, imaging approaches have shown great potential for high-throughput plant phenotyping, resulting in more attention paid to imaging-based plant phenotyping. With this increased amount of image data, it has become urgent to develop robust analytical tools that can extract phenotypic traits accurately and rapidly. The goal of this review is to provide a comprehensive overview of the latest studies using deep convolutional neural networks (CNNs) in plant phenotyping applications. We specifically review the use of various CNN architecture for plant stress evaluation, plant development, and postharvest quality assessment. We systematically organize the studies based on technical developments resulting from imaging classification, object detection, and image segmentation, thereby identifying state-of-the-art solutions for certain phenotyping applications. Finally, we provide several directions for future research in the use of CNN architecture for plant phenotyping purposes.

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Section: Plant Phenomicsmentioning

confidence: 99%

Convolutional Neural Networks for Image-Based High-Throughput Plant Phenotyping: A Review

2020

Plant Phenomics

256

180

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“…Currently, there are many deep learning tasks that need to process data with graph structures. Convolutional neural networks (CNNs) [17] have been successfully developed in the field of computer vision [18,19] but are unable to process graph structured data [20]. The method used in this paper is called a graph convolutional network (GCN).…”

Section: Graph Neural Networkmentioning

confidence: 99%

Multilabel Classification Based on Graph Neural Networks

Ye¹,

Wang²

2022

Artificial Intelligence

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Typical Laplacian embedding focuses on building Laplacian matrices prior to minimizing weights of connected graph components. However, for multilabel problems, it is difficult to determine such Laplacian graphs owing to multiple relations between vertices. Unlike typical approaches that require precomputed Laplacian matrices, this chapter presents a new method for automatically constructing Laplacian graphs during Laplacian embedding. By using trace minimization techniques, the topology of the Laplacian graph can be learned from input data, subsequently creating robust Laplacian embedding and influencing graph convolutional networks. Experiments on different open datasets with clean data and Gaussian noise were carried out. The noise level ranged from 6% to 12% of the maximum value of each dataset. Eleven different multilabel classification algorithms were used as the baselines for comparison. To verify the performance, three evaluation metrics specific to multilabel learning are proposed because multilabel learning is much more complicated than traditional single-label settings; each sample can be associated with multiple labels. The experimental results show that the proposed method performed better than the baselines, even when the data were contaminated by noise. The findings indicate that the proposed method is reliably robust against noise.

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“…For instance, Wan et al (2020) adopted the Faster R-CNN ( Ren et al, 2015 ) to detect apples, oranges, and mangoes more accurately by improving the convolutional and pooling layers ( Wan and Goudos, 2020 ). Mai et al (2020) proposed a novel Faster R-CNN by merging multiple classifier fusion strategies; the improved model identified small fruit compared with other detection models. Tian et al (2019a) improved the YOLO-V3 ( Redmon and Farhadi, 2018 ) model with the DenseNet ( Huang et al, 2017 ) network to process low-resolution feature layers for apple detection.…”

Section: Introductionmentioning

confidence: 99%

Fruit Detection and Pose Estimation for Grape Cluster–Harvesting Robot Using Binocular Imagery Based on Deep Neural Networks

et al. 2021

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Reliable and robust fruit-detection algorithms in nonstructural environments are essential for the efficient use of harvesting robots. The pose of fruits is crucial to guide robots to approach target fruits for collision-free picking. To achieve accurate picking, this study investigates an approach to detect fruit and estimate its pose. First, the state-of-the-art mask region convolutional neural network (Mask R-CNN) is deployed to segment binocular images to output the mask image of the target fruit. Next, a grape point cloud extracted from the images was filtered and denoised to obtain an accurate grape point cloud. Finally, the accurate grape point cloud was used with the RANSAC algorithm for grape cylinder model fitting, and the axis of the cylinder model was used to estimate the pose of the grape. A dataset was acquired in a vineyard to evaluate the performance of the proposed approach in a nonstructural environment. The fruit detection results of 210 test images show that the average precision, recall, and intersection over union (IOU) are 89.53, 95.33, and 82.00%, respectively. The detection and point cloud segmentation for each grape took approximately 1.7 s. The demonstrated performance of the developed method indicates that it can be applied to grape-harvesting robots.

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Faster R-CNN With Classifier Fusion for Automatic Detection of Small Fruits

Cited by 46 publications

References 39 publications

Convolutional Neural Networks for Image-Based High-Throughput Plant Phenotyping: A Review

Convolutional Neural Networks for Image-Based High-Throughput Plant Phenotyping: A Review

Multilabel Classification Based on Graph Neural Networks

Fruit Detection and Pose Estimation for Grape Cluster–Harvesting Robot Using Binocular Imagery Based on Deep Neural Networks

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