Immature citrus fruit detection based on local binary pattern feature and hierarchical contour analysis

Lü, Jun; Lee, Won Suk; Gan, Hao; Hu, Xiuwen

doi:10.1016/j.biosystemseng.2018.04.009

Cited by 66 publications

(36 citation statements)

References 27 publications

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“…Normally, statistical characterization would be used to extract the texture feature of superpixels due to its irregular shape. LBP, however, had shown its superiority in texture extraction [16][17][18][19][20] as discussed in section 1, thus the global LBP was computed instead of calculating statistical gray features of superpixels directly in this paper, hence the differences of the edge between superpixels were taken into account under which circumstance the targets were able to be segmented more precisely. The texture features of superpixels were extracted by acquiring LBP statistical histogram.…”

Section: Texture Featurementioning

confidence: 99%

“…Wang et al [19] used discrete wavelet transform, K-means clustering, and CHT to detect citrus with detection accuracy, false positive and miss rate of 85.6%, 11.8% and14.4%, respectively. Lu et al [20] detected local intensity maxima in the G channel of images taken in low natural light conditions with a flashlight and LBP features around them were extracted as an input of an ensemble random under-sampling with AdaBoost (RUSBoost) to get positive predictions. The hierarchical contour maps around positive predictions which considered as candidates were extracted and fitted with CHT to get the final targets if its radius were in a predetermined range.…”

Section: Introductionmentioning

confidence: 99%

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Superpixel-based segmentation algorithm for mature citrus

Yang

Chen

Xun

et al. 2020

International Journal of Agricultural and Biological Engineering

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With the decrease of agricultural labors and the increase in production costs, harvesting robots have become a research hotspot in recent years. To guide harvesting robots to pick mature citrus more precisely under variable illumination conditions, an image segmentation algorithm based on superpixel was proposed. Efficient simple linear iterative clustering (SLIC) algorithm which takes similarity of adjacent pixels into account was adopted to segment the images captured under variable illumination conditions into superpixels. The color and texture features of these superpixels were extracted and fused into feature vectors as descriptors to train backpropagation neural networks (BPNN) classifier in the next step. The adjacency information of superpixels was considered by calculating the global-local binary pattern (LBP) in R component images when extracting texture features. To accelerate the classification process, the mean of Cr-Cb image was utilized to find superpixels of interest which were regarded as candidates of citrus superpixels. These candidates were then classified by a pre-trained BPNN model with superpixel-level accuracy of 98.77% and pixel-level accuracy of 94.96%, while the average time to segment one image was 0.4778 s. Therefore, the results indicated that a superpixel-based segmentation algorithm toward citrus images had decent light robustness as well as high accuracy that could guide harvesting robot to pick mature citrus efficiently.

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Section: Texture Featurementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Superpixel-based segmentation algorithm for mature citrus

Yang

Chen

Xun

et al. 2020

International Journal of Agricultural and Biological Engineering

View full text Add to dashboard Cite

show abstract

“…There are some machine learning based techologies for detection tasks, such as those reported in [25]- [30]. To detect and count immature citrus fruits, Lu et al [9] extracted features of local binary pattern (LBP) and detected local intensity maxima around the immature fruits. Benalia et al [31] developed a system to improve the quality control and sorting of dried fruits of fig (Ficus carica).…”

Section: B Machine Learningmentioning

confidence: 99%

“…In this article, we generated seven groups of Negative patches (NP) by randomly tailoring a fixed rectangle size from negative images of a dataset. The sizes of these seven NP groups are n × n pixels n ∈ [1,9] and n ∈ Z . And we augmented our dataset in Algorithm 1.…”

Section: Fusion Augmentationmentioning

confidence: 99%

Multi-Task Cascaded Convolutional Networks Based Intelligent Fruit Detection for Designing Automated Robot

et al. 2019

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Effective and efficient fruit detection is considered crucial for designing automated robot (AuRo) for yield estimation, disease control, harvesting, sorting, and grading. Several fruit detection schemes for designing AuRo have been developed during the last decades. However, conventional fruit detection methods are deficient in the real-time response, accuracy, and extensibility. This paper proposes an improved multi-task cascaded convolutional network-based intelligent fruit detection method. This method has the capability to make the AuRo work in real time with high accuracy. Moreover, based on the relationship between the diversity samples of the dataset and the parameters of neural networks' evolution, this paper presents an improved augmented method, a procedure that is based on image fusion to improve the detector performance. The experiment results demonstrated that the proposed detector performed immaculately both in terms of accuracy and time-cost. Furthermore, the extensive experiment also demonstrated that the proposed technique has the capacity and good portability to work with other akin objects conveniently. INDEX TERMS Fruit detection, real-time, cascaded convolutional networks, automated robot.

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“…It is difficult to solve problems such as overlap and occlusion, and it is easily affected by lighting conditions. The effect is poor in a complex background, resulting in relatively high cost and poor applicability of these methods [ 2 , 3 , 4 , 5 ].…”

Section: Introductionmentioning

confidence: 99%

Pine Cone Detection Using Boundary Equilibrium Generative Adversarial Networks and Improved YOLOv3 Model

Luo

Zhang

2020

Sensors

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The real-time detection of pine cones in Korean pine forests is not only the data basis for the mechanized picking of pine cones, but also one of the important methods for evaluating the yield of Korean pine forests. In recent years, there has been a certain number of detection accuracy for image processing of fruits in trees using deep-learning methods, but the overall performance of these methods has not been satisfactory, and they have never been used in the detection of pine cones. In this paper, a pine cone detection method based on Boundary Equilibrium Generative Adversarial Networks (BEGAN) and You Only Look Once (YOLO) v3 mode is proposed to solve the problems of insufficient data set, inaccurate detection result and slow detection speed. First, we use traditional image augmentation technology and generative adversarial network BEGAN to implement data augmentation. Second, we introduced a densely connected network (DenseNet) structure in the backbone network of YOLOv3. Third, we expanded the detection scale of YOLOv3, and optimized the loss function of YOLOv3 using the Distance-IoU (DIoU) algorithm. Finally, we conducted a comparative experiment. The experimental results show that the performance of the model can be effectively improved by using BEGAN for data augmentation. Under same conditions, the improved YOLOv3 model is better than the Single Shot MultiBox Detector (SSD), the faster-regions with convolutional neural network (Faster R-CNN) and the original YOLOv3 model. The detection accuracy reaches 95.3%, and the detection efficiency is 37.8% higher than that of the original YOLOv3.

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Immature citrus fruit detection based on local binary pattern feature and hierarchical contour analysis

Cited by 66 publications

References 27 publications

Superpixel-based segmentation algorithm for mature citrus

Superpixel-based segmentation algorithm for mature citrus

Multi-Task Cascaded Convolutional Networks Based Intelligent Fruit Detection for Designing Automated Robot

Pine Cone Detection Using Boundary Equilibrium Generative Adversarial Networks and Improved YOLOv3 Model

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