Semantic Segmentation of Litchi Branches Using DeepLabV3+ Model

Peng, Hongxing; Xue, Chao; Shao, Yuanyuan; Chen, Ke-Yin; Xiong, Juntao; Xie, Zhihua; Zhang, Liuhong

doi:10.1109/access.2020.3021739

Cited by 85 publications

(33 citation statements)

References 22 publications

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“…(1) target classification, that is, to determine the category of the target in the image, such as for fruit variety recognition, disease variety recognition, and fruit grading; (2) target detection, that is, detecting and locating the target in the image that is commonly used in target detection models including R-CNN, Fast R-CNN, Faster R-CNN YOLO, etc., which are often used to detect fruits or disease in the image; (3) semantic segmentation, which can accurately determine the category of each pixel. For example, in [18], the finely trained DeepLabV3+ could represent the background pixels as black and the target as red. In summary, the model related to target recognition is not applicable to this research.…”

Section: Motivationmentioning

confidence: 99%

“…However, there are a variety of semantic segmentation models such as U-Net, FCN, SegNet, PSPNet, and DeepLab series, etc. Among the various semantic segmentation models, DeepLabV3+ had been extensively used in many studies [18][19][20] and can obtain more accurate contours. Therefore, DeepLabV3+ was chosen as the model for the recognition of grapes.…”

Section: Motivationmentioning

confidence: 99%

“…(2) target detection, that is, detecting and locating the target in the image that is commonly used in target detection models including R-CNN, Fast R-CNN, Faster R-CNN YOLO, etc., which are often used to detect fruits or disease in the image; (3) semantic segmentation, which can accurately determine the category of each pixel. For example, in [18], the finely trained DeepLabV3+ could represent the background pixels as black and the target as red.…”

Section: Motivationmentioning

confidence: 99%

See 2 more Smart Citations

Segmentation of Overlapping Grape Clusters Based on the Depth Region Growing Method

2021

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Accurately extracting the grape cluster at the front of overlapping grape clusters is the primary problem of the grape-harvesting robot. To solve the difficult problem of identifying and segmenting the overlapping grape clusters in the cultivation environment of a trellis, a simple method based on the deep learning network and the idea of region growing is proposed. Firstly, the region of grape in an RGB image was obtained by the finely trained DeepLabV3+ model. The idea of transfer learning was adopted when training the network with a limited number of training sets. Then, the corresponding region of the grape in the depth image captured by RealSense D435 was processed by the proposed depth region growing algorithm (DRG) to extract the front cluster. The depth region growing method uses the depth value instead of gray value to achieve clustering. Finally, it fils the holes in the clustered region of interest, extracts the contours, and maps the obtained contours to the RGB image. The images captured by RealSense D435 in a natural trellis environment were adopted to evaluate the performance of the proposed method. The experimental results showed that the recall and precision of the proposed method were 89.2% and 87.5%, respectively. The demonstrated performance indicated that the proposed method could satisfy the requirements of practical application for robotic grape harvesting.

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Section: Motivationmentioning

confidence: 99%

Section: Motivationmentioning

confidence: 99%

Section: Motivationmentioning

confidence: 99%

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Segmentation of Overlapping Grape Clusters Based on the Depth Region Growing Method

2021

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“…First, the SO-PMI distinguishes positive emotional English texts from negative emotional English texts, classifies and calculates the texts, and then subtracts them to obtain the emotional tendency of the English text [ 20 ]. However, the selection of different types of English texts requires manual intervention and high professionalism, so personnel selection is very meticulous.…”

Section: Model Establishment and Scheme Designmentioning

confidence: 99%

Application of Dual‐Channel Convolutional Neural Network Algorithm in Semantic Feature Analysis of English Text Big Data

2021

Computational Intelligence and Neuroscience

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The current Internet data explosion is expecting an ever-higher demand for text emotion analysis that greatly facilitates public opinion analysis and trend prediction, among others. Therefore, this paper proposes to use a dual-channel convolutional neural network (DCNN) algorithm to analyze the semantic features of English text big data. Following the analysis of the effect of CNN, artificial neural network (ANN), and recurrent neural network (RNN) on English text data analysis, the more effective long short-term memory (LSTM) and the gated recurrent unit (GRU) neural network (NN) are introduced, and each network is combined with the dual-channel CNN, respectively, and comprehensively analyzed under comparative experiments. Second, the semantic features of English text big data are analyzed through the improved SO-pointwise mutual information (SO-PMI) algorithm. Finally, the ensemble dual-channel CNN model is established. Under the comparative experiment, GRU NN has a better feature detection effect than LSTM NN, but the performance increase from dual-channel CNN to GRU NN + dual-channel CNN is not obvious. Under the comparative analysis of GRU NN + dual-channel CNN model and LSTM NN + dual-channel CNN model, GRU NN + dual-channel CNN model ensures the high accuracy of semantic feature analysis and improves the analysis speed of the model. Further, after the attention mechanism is added to the GRU NN + dual-channel CNN model, the accuracy of semantic feature analysis of the model is improved by nearly 1.3%. Therefore, the ensemble model of GRU NN + dual-channel CNN + attention mechanism is more suitable for semantic feature analysis of English text big data. The results will help the e-commerce platform to analyze the evaluation language and semantic features for the current network English short texts.

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“…Previous studies on the identification of individual trees have been focused on several species, including citrus [4,5,8,[25][26][27][28], apple [23], palm [10,14,29], cranberry [21], and urban trees [13,24]. However, although there are studies on the semantic segmentation of litchi flowers [30] and branches [31], the studies on litchi canopy segmentation based on remote sensing, as far as we know, have not been proposed.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Instance Segmentation Method of Litchi Canopy from UAV-Acquired Images

Lan

Yang

et al. 2021

Remote Sensing

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Instance segmentation of fruit tree canopies from images acquired by unmanned aerial vehicles (UAVs) is of significance for the precise management of orchards. Although deep learning methods have been widely used in the fields of feature extraction and classification, there are still phenomena of complex data and strong dependence on software performances. This paper proposes a deep learning-based instance segmentation method of litchi trees, which has a simple structure and lower requirements for data form. Considering that deep learning models require a large amount of training data, a labor-friendly semi-auto method for image annotation is introduced. The introduction of this method allows for a significant improvement in the efficiency of data pre-processing. Facing the high requirement of a deep learning method for computing resources, a partition-based method is presented for the segmentation of high-resolution digital orthophoto maps (DOMs). Citrus data is added to the training set to alleviate the lack of diversity of the original litchi dataset. The average precision (AP) is selected to evaluate the metric of the proposed model. The results show that with the help of training with the litchi-citrus datasets, the best AP on the test set reaches 96.25%.

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Semantic Segmentation of Litchi Branches Using DeepLabV3+ Model

Cited by 85 publications

References 22 publications

Segmentation of Overlapping Grape Clusters Based on the Depth Region Growing Method

Segmentation of Overlapping Grape Clusters Based on the Depth Region Growing Method

Application of Dual‐Channel Convolutional Neural Network Algorithm in Semantic Feature Analysis of English Text Big Data

Deep Learning-Based Instance Segmentation Method of Litchi Canopy from UAV-Acquired Images

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