Location and Recognition Fruit Trees Based on Binocular Stereo Vision

Zhao, Xueqing; Gao, Yuanyuan; Wang, Songlin; Wang, Xiu; Fan, Pingzhi; Feng, Qianjin

doi:10.1007/978-3-030-06179-1_46

Cited by 2 publications

(4 citation statements)

References 7 publications

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“…By solving the system of equations formed by combining the above equations, the coordinates of the intersection point (x, y) of these two lines can be obtained. The calculation formula is shown as follows (13).…”

Section: Startmentioning

confidence: 99%

“…Use formula (13) to solve for the intersection coordinates of four straight lines, and then output the coordinates of the three corner points to be solved based on the distribution characteristics of the corner points of the suspension rod grasped by the bridge erecting machine. The specific steps for finding the corner points are as follows:…”

Section: Startmentioning

confidence: 99%

“…Then, the RANSAC algorithm was utilized to select the initial set of matching points based on the polar coordinate geometric model, resulting in a well‐matched set. Zhao et al 13 utilized binocular vision to identify the contour and distance information of fruit trees. They proposed a left‐right image feature point matching method based on cosine distance and vector modulus.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Improved method for positioning crane grab boom corner points using Hough transform and k‐means clustering

Wang,

Wan,

Zhao

et al. 2023

Engineering Reports

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In the process of automatic grabbing of bridge segment beams, it is crucial to accurately locate and align the corner points of the crane's boom with the beam's lifting holes. This requires the utilization of image processing techniques to precisely detect and locate the corner points of the crane's boom. Existing feature matching methods face challenges such as low detection accuracy and unsuitability for this specific scenario. This article proposes a novel approach for corner point localization by using the intersection points of lines, facilitating the matching of feature points between left and right images. The method consists of three steps: first, a grayscale difference map is constructed by utilizing the R and G channels of the RGB color space. This enhances the bimodal characteristics of the grayscale histograms between the foreground and background, which facilitates the subsequent binarization process. Additionally, opening and closing operations are employed to remove small artifacts from the Canny edge detection results, effectively reducing noise. Second, an adaptive thresholding method based on the mean and variance of Hough transform voting scores is proposed. This method filters out interference lines from the clustering results by selecting appropriate voting scores. Furthermore, an improved centroid calculation method is introduced, which utilizes weighted formulas based on different proportions of voting scores. These weighted formulas replace the original clustering centroids as the basis for line fitting. Finally, the corner coordinates of the crane's boom are computed based on the line fitting results, and the recognition accuracy is compared under different lighting conditions. Experimental results demonstrate that the proposed algorithm exhibits smaller detection errors and higher robustness compared to other corner detection algorithms, particularly when there are numerous interference edge points in the edge detection results. The computed corner coordinates achieve pixel‐level accuracy. The algorithm performs optimally under strong supplementary lighting conditions, with an average detection error percentage of 97.1% within 0–2 pixels and a recognition accuracy of 98.6%. The recognition success rates under different lighting conditions are all above 92.9%. This method is superior to traditional corner detection methods, meets the requirements for automatic grabbing of the boom, and holds practical engineering value. It provides a basis for addressing the accuracy and robustness challenges of crane algorithms influenced by multiple environmental factors.

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Section: Startmentioning

confidence: 99%

Section: Startmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Improved method for positioning crane grab boom corner points using Hough transform and k‐means clustering

Wang,

Wan,

Zhao

et al. 2023

Engineering Reports

View full text Add to dashboard Cite

show abstract

“…They also performed the motion control of the manipulator of a tomato harvesting robot on the basis of 3D coordinates. According to the point matching method for the features of left and right images based on the cosine distance and vector modulus, binocular vision plus an optimized SIFT stereo matching algorithm were exploited to identify the contour and distance data of fruit trees [ 29 ]. Nevertheless, although the above crop recognition and detection methods based on traditional machine vision and stereo imaging are relatively easy to implement, the detection accuracy is not satisfying.…”

Section: Related Workmentioning

confidence: 99%

Detection of Green Asparagus Using Improved Mask R-CNN for Automatic Harvesting

Liu

Wang

et al. 2022

Sensors

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Advancements in deep learning and computer vision have led to the discovery of numerous effective solutions to challenging problems in the field of agricultural automation. With the aim to improve the detection precision in the autonomous harvesting process of green asparagus, in this article, we proposed the DA-Mask RCNN model, which utilizes the depth information in the region proposal network. Firstly, the deep residual network and feature pyramid network were combined to form the backbone network. Secondly, the DA-Mask RCNN model added a depth filter to aid the softmax function in anchor classification. Afterwards, the region proposals were further processed by the detection head unit. The training and test images were mainly acquired from different regions in the basin of the Yangtze River. During the capturing process, various weather and illumination conditions were taken into account, including sunny weather, sunny but overshadowed conditions, cloudy weather, and daytime greenhouse conditions as well as nighttime greenhouse conditions. Performance experiments, comparison experiments, and ablation experiments were carried out using the five constructed datasets to verify the effectiveness of the proposed model. Precision, recall, and F1-score values were applied to evaluate the performances of different approaches. The overall experimental results demonstrate that the balance of the precision and speed of the proposed DA-Mask RCNN model outperform those of existing algorithms.

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Location and Recognition Fruit Trees Based on Binocular Stereo Vision

Cited by 2 publications

References 7 publications

Improved method for positioning crane grab boom corner points using Hough transform and k‐means clustering

Improved method for positioning crane grab boom corner points using Hough transform and k‐means clustering

Detection of Green Asparagus Using Improved Mask R-CNN for Automatic Harvesting

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