YOLOv3-Litchi Detection Method of Densely Distributed Litchi in Large Vision Scenes

Wang, Hongjun; Dong, Lizhong; Zhou, Huaibei; Luo, Liang; Lin, Guimin; Wu, Jinlong; Tang, Yunchao

doi:10.1155/2021/8883015

Cited by 30 publications

(19 citation statements)

References 48 publications

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“…The frame rate of the stereo depth camera for detecting palm fruits, durian fruits, and pineapples reached 16.71 frames per second ( Zhang et al, 2021 ). Wang et al (2021) described a modified YOLOv3-Litchi model for detecting densely distributed lychee fruits in a large visual scene, where the mean precision was 87.43%. Wu et al (2020) reported a real-time apple flower detector method using the channel-pruned YOLOv4 deep learning algorithm, which had an mAP of 97.31%.…”

Section: Introductionmentioning

confidence: 99%

Multi-Target Recognition of Bananas and Automatic Positioning for the Inflorescence Axis Cutting Point

Duan

Chen

et al. 2021

Front. Plant Sci.

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Multi-target recognition and positioning using robots in orchards is a challenging task in modern precision agriculture owing to the presence of complex noise disturbance, including wind disturbance, changing illumination, and branch and leaf shading. To obtain the target information for a bud-cutting robotic operation, we employed a modified deep learning algorithm for the fast and precise recognition of banana fruits, inflorescence axes, and flower buds. Thus, the cutting point on the inflorescence axis was identified using an edge detection algorithm and geometric calculation. We proposed a modified YOLOv3 model based on clustering optimization and clarified the influence of front-lighting and backlighting on the model. Image segmentation and denoising were performed to obtain the edge images of the flower buds and inflorescence axes. The spatial geometry model was constructed on this basis. The center of symmetry and centroid were calculated for the edges of the flower buds. The equation for the position of the inflorescence axis was established, and the cutting point was determined. Experimental results showed that the modified YOLOv3 model based on clustering optimization showed excellent performance with good balance between speed and precision both under front-lighting and backlighting conditions. The total pixel positioning error between the calculated and manually determined optimal cutting point in the flower bud was 4 and 5 pixels under the front-lighting and backlighting conditions, respectively. The percentage of images that met the positioning requirements was 93 and 90%, respectively. The results indicate that the new method can satisfy the real-time operating requirements for the banana bud-cutting robot.

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Section: Introductionmentioning

confidence: 99%

Multi-Target Recognition of Bananas and Automatic Positioning for the Inflorescence Axis Cutting Point

Duan

Chen

et al. 2021

Front. Plant Sci.

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“…To free the farmers from heavy work, reduce repetitive operations, and avoid the harm caused by certain operations, there is a great need for fruit-picking robots [3,4]. A fruit-picking robot is a type of robot that is designed to move through an entire field and pick fruits on its own [5][6][7][8][9][10][11][12][13]. In our previous study, a row-following system based on machine vision for a banana-picking robot was developed.…”

Section: Introductionmentioning

confidence: 99%

Row End Detection and Headland Turning Control for an Autonomous Banana-Picking Robot

et al. 2021

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A row-following system based on machine vision for a picking robot was designed in our previous study. However, the visual perception could not provide reliable information during headland turning according to the test results. A complete navigation system for a picking robot working in an orchard needs to support accurate row following and headland turning. To fill this gap, a headland turning method for an autonomous picking robot was developed in this paper. Three steps were executed during headland turning. First, row end was detected based on machine vision. Second, the deviation was further reduced before turning using the designed fast posture adjustment algorithm based on satellite information. Third, a curve path tracking controller was developed for turning control. During the MATLAB simulation and experimental test, different controllers were developed and compared with the designed method. The results show that the designed turning method enabled the robot to converge to the path more quickly and remain on the path with lower radial errors, which eventually led to reductions in time, space, and deviation during headland turning.

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“…To design strategies to improve picking efficiency, it is important to examine the use of intelligent robots for harvesting fruits. For the past 30 years, scholars from different parts of the world have studied fruit-harvesting robots, including harvesting robots for strawberry [7], cucumber [8], apple [9], sweet pepper [10], citrus [11], litchi [12,13], banana [14], mango [15], and so on. However, up to now, commercial automatic harvesting robots are still rarely reported, and one of the main reasons is low location accuracy in unstructured orchards.…”

Section: Introductionmentioning

confidence: 99%

SwinGD: A Robust Grape Bunch Detection Model Based on Swin Transformer in Complex Vineyard Environment

et al. 2021

Self Cite

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Accurate recognition of fruits in the orchard is an important step for robot picking in the natural environment, since many CNN models have a low recognition rate when dealing with irregularly shaped and very dense fruits, such as a grape bunch. It is a new trend to use a transformer structure and apply it to a computer vision domain for image processing. This paper provides Swin Transformer and DETR models to achieve grape bunch detection. Additionally, they are compared with traditional CNN models, such as Faster-RCNN, SSD, and YOLO. In addition, the optimal number of stages for a Swin Transformer through experiments is selected. Furthermore, the latest YOLOX model is also used to make a comparison with the Swin Transformer, and the experimental results show that YOLOX has higher accuracy and better detection effect. The above models are trained under red grape datasets collected under natural light. In addition, the dataset is expanded through image data augmentation to achieve a better training effect. After 200 epochs of training, SwinGD obtained an exciting mAP value of 94% when IoU = 0.5. In case of overexposure, overdarkness, and occlusion, SwinGD can recognize more accurately and robustly compared with other models. At the same time, SwinGD still has a better effect when dealing with dense grape bunches. Furthermore, 100 pictures of grapes containing 655 grape bunches are downloaded from Baidu pictures to detect the effect. The Swin Transformer has an accuracy of 91.5%. In order to verify the universality of SwinGD, we conducted a test under green grape images. The experimental results show that SwinGD has a good effect in practical application. The success of SwinGD provides a new solution for precision harvesting in agriculture.

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YOLOv3-Litchi Detection Method of Densely Distributed Litchi in Large Vision Scenes

Cited by 30 publications

References 48 publications

Multi-Target Recognition of Bananas and Automatic Positioning for the Inflorescence Axis Cutting Point

Multi-Target Recognition of Bananas and Automatic Positioning for the Inflorescence Axis Cutting Point

Row End Detection and Headland Turning Control for an Autonomous Banana-Picking Robot

SwinGD: A Robust Grape Bunch Detection Model Based on Swin Transformer in Complex Vineyard Environment

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