Feature Extraction and Recognition Based on Machine Vision Application in Lotus Picking Robot

Tang, Shuping; Zhao, Debin; Jia, Weikuan; Chen, Yu; Ji, Wei; Ruan, Chengzhi

doi:10.1007/978-3-319-48357-3_46

Cited by 6 publications

(4 citation statements)

References 13 publications

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“…However, these noises can have a serious influence on the recognition. us, we need to take the relevant measures to eliminate the influence of the noise [9]. In this paper, we have applied the "Corrosion-remove-expansion" method effectively.…”

Section: Image Segmentationmentioning

confidence: 99%

Target Recognition and Trajectory Planning of Apple Harvesting Robot considering Color Multimedia Image Segmentation Algorithm

Wang

2021

Advances in Multimedia

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For the purpose of significantly reducing the processing time of the apple harvesting robot during the harvesting process, it is highly necessary to carry out the corresponding studies on the methods for rapid recognition and trajectory planning. Through the comprehensive application of information relevance, the image processing area can be reduced. For image recognition and trajectory planning, the related template matching algorithm for removing the mean value and normalization product can be adopted, and segmentation methods based on different threshold values can be used for the realization of the effect. Subsequently, the comparative experiments are properly carried out to verify the effectiveness of the method used.

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Section: Image Segmentationmentioning

confidence: 99%

Target Recognition and Trajectory Planning of Apple Harvesting Robot considering Color Multimedia Image Segmentation Algorithm

Wang

2021

Advances in Multimedia

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“…Traditional image segmentation techniques are generally based on image preprocessing, feature extraction, and classification. For example, Tang [3] used the color and shape characteristics of the lotus seedpods with the combination of the K-Means clustering method to segment lotus seedpods in a complex environment. Following this idea, different machine learning classifiers were used to identify the maturity of fruits based on the color and texture features of fruits [4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

An Improved YOLOv8 Model for Lotus Seedpod Instance Segmentation in the Lotus Pond Environment

Ma,

Zhao,

Fan

et al. 2024

Agronomy

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Lotus seedpod maturity detection and segmentation in pond environments play a significant role in yield prediction and picking pose estimation for lotus seedpods. However, it is a great challenge to accurately detect and segment lotus seedpods due to insignificant phenotypic differences between the adjacent maturity, changing illumination, overlap, and occlusion of lotus seedpods. The existing research pays attention to lotus seedpod detection while ignoring maturity detection and segmentation problems. Therefore, a semantic segmentation dataset of lotus seedpods was created, where a copy-and-paste data augmentation tool was employed to eliminate the class-imbalanced problem and improve model generalization ability. Afterwards, an improved YOLOv8-seg model was proposed to detect and segment the maturity of lotus seedpods. In the model, the convolutional block attention module (CBAM) was embedded in the neck network to extract distinguished features of different maturity stages with negligible computation cost. Wise-Intersection over Union (WIoU) regression loss function was adopted to refine the regression inference bias and improve the bounding box prediction accuracy. The experimental results showed that the proposed YOLOv8-seg model provides an effective method for “ripe” and “overripe” lotus seedpod detection and instance segmentation, where the mean average precision of segmentation mask (mAPmask) reaches 97.4% and 98.6%, respectively. In addition, the improved YOLOv8-seg exhibits high robustness and adaptability to complex illumination in a challenging environment. Comparative experiments were conducted using the proposed YOLOv8-seg and other state-of-the-art instance segmentation methods. The results showed that the improved model is superior to the Mask R-CNN and YOLACT models, with recall, precision, mAPbox and mAPmask being 96.5%, 94.3%, 97.8%, and 98%, respectively. The average running time and weight size of the proposed model are 25.9 ms and 7.4 M, respectively. The proposed model obtained the highest mAP for lotus seedpod maturity detection and segmentation while maintaining an appropriate model size and speed. Furthermore, based on the obtained segmentation model, 3D visualization of the lotus pond scene is performed, and cloud point of lotus seedpods is generated, which provides a theoretical foundation for robot harvesting in the lotus pond.

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“…During the development of the Zanthoxylum harvester, it was intended to be lightweight and easy to operate, with semi-automated portability [6]. Although these studies have, to some extent, improved the efficiency of Zanthoxylum harvesting and reduced the labor intensity, the lack of an automatic recognition and positioning system for Zanthoxylum makes it difficult to achieve fully automatic harvesting [7,8]. Many scholars have proposed target detection and localization algorithms for Zanthoxylum based on computer vision.…”

Section: Introductionmentioning

confidence: 99%

Design and Experiment of a Visual Detection System for Zanthoxylum-Harvesting Robot Based on Improved YOLOv5 Model

et al. 2023

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In order to achieve accurate detection of mature Zanthoxylum in their natural environment, a Zanthoxylum detection network based on the YOLOv5 object detection model was proposed. It addresses the issues of irregular shape and occlusion caused by the growth of Zanthoxylum on trees and the overlapping of Zanthoxylum branches and leaves with the fruits, which affect the accuracy of Zanthoxylum detection. To improve the model’s generalization ability, data augmentation was performed using different methods. To enhance the directionality of feature extraction and enable the convolution kernel to be adjusted according to the actual shape of each Zanthoxylum cluster, the coordinate attention module and the deformable convolution module were integrated into the YOLOv5 network. Through ablation experiments, the impacts of the attention mechanism and deformable convolution on the performance of YOLOv5 were compared. Comparisons were made using the Faster R-CNN, SSD, and CenterNet algorithms. A Zanthoxylum harvesting robot vision detection platform was built, and the visual detection system was tested. The experimental results showed that using the improved YOLOv5 model, as compared to the original YOLOv5 network, the average detection accuracy for Zanthoxylum in its natural environment was increased by 4.6% and 6.9% in terms of mAP@0.5 and mAP@0.5:0.95, respectively, showing a significant advantage over other network models. At the same time, on the test set of Zanthoxylum with occlusions, the improved model showed increased mAP@0.5 and mAP@0.5:0.95 by 5.4% and 4.7%, respectively, compared to the original model. The improved model was tested on a mobile picking platform, and the results showed that the model was able to accurately identify mature Zanthoxylum in its natural environment at a detection speed of about 89.3 frames per second. This research provides technical support for the visual detection system of intelligent Zanthoxylum-harvesting robots.

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Feature Extraction and Recognition Based on Machine Vision Application in Lotus Picking Robot

Cited by 6 publications

References 13 publications

Target Recognition and Trajectory Planning of Apple Harvesting Robot considering Color Multimedia Image Segmentation Algorithm

Target Recognition and Trajectory Planning of Apple Harvesting Robot considering Color Multimedia Image Segmentation Algorithm

An Improved YOLOv8 Model for Lotus Seedpod Instance Segmentation in the Lotus Pond Environment

Design and Experiment of a Visual Detection System for Zanthoxylum-Harvesting Robot Based on Improved YOLOv5 Model

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