Fresh Tea Sprouts Detection via Image Enhancement and Fusion SSD

Chen, Bin; Yan, Jili; Ke, Wang

doi:10.1155/2021/6614672

Cited by 9 publications

(2 citation statements)

References 26 publications

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“…This method separately inputs the original and enhanced images into a ResNet50 subnetwork and improves detection accuracy through score fusion. This method achieved an AP of 92.8% [16]. Xu et al combined YOLOv3 with DenseNet201 to quickly and accurately detect and classify tea buds, achieving 82.58% precision on the top-shot dataset and 99.28% precision on the side-shot dataset [17].…”

Section: Introductionmentioning

confidence: 99%

Tea Bud Detection and 3D Pose Estimation in the Field with a Depth Camera Based on Improved YOLOv5 and the Optimal Pose-Vertices Search Method

Chen

et al. 2023

Agriculture

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The precise detection and positioning of tea buds are among the major issues in tea picking automation. In this study, a novel algorithm for detecting tea buds and estimating their poses in a field environment was proposed by using a depth camera. This algorithm introduces some improvements to the YOLOv5l architecture. A Coordinate Attention Mechanism (CAM) was inserted into the neck part to accurately position the elements of interest, a BiFPN was used to enhance the small object detection ability, and a GhostConv module replaced the original Conv module in the backbone to reduce the model size and speed up model inference. After testing, the proposed detection model achieved an mAP of 85.2%, a speed of 87.71 FPS, a parameter number of 29.25 M, and a FLOPs value of 59.8 G, which are all better than those achieved with the original model. Next, an optimal pose-vertices search method (OPVSM) was developed to estimate the pose of tea by constructing a graph model to fit the pointcloud. This method could accurately estimate the poses of tea buds, with an overall accuracy of 90%, and it was more flexible and adaptive to the variations in tea buds in terms of size, color, and shape features. Additionally, the experiments demonstrated that the OPVSM could correctly establish the pose of tea buds through pointcloud downsampling by using voxel filtering with a 2 mm × 2 mm × 1 mm grid, and this process could effectively reduce the size of the pointcloud to smaller than 800 to ensure that the algorithm could be run within 0.2 s. The results demonstrate the effectiveness of the proposed algorithm for tea bud detection and pose estimation in a field setting. Furthermore, the proposed algorithm has the potential to be used in tea picking robots and also can be extended to other crops and objects, making it a valuable tool for precision agriculture and robotic applications.

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Section: Introductionmentioning

confidence: 99%

Tea Bud Detection and 3D Pose Estimation in the Field with a Depth Camera Based on Improved YOLOv5 and the Optimal Pose-Vertices Search Method

Chen

et al. 2023

Agriculture

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“…In order to obtain more discerning tea bud features, a variety of semantic segmentation models were used for performance comparison (Qian et al [6]). Chen et al [7] used the fusion of a single-shot detector (SSD) and an image enhancement algorithm to improve the speed and accuracy of tea detection.…”

Section: Introductionmentioning

confidence: 99%

A Machine Vision-Based Method for Tea Buds Segmentation and Picking Point Location Used on a Cloud Platform

Yang

Sun

et al. 2023

Agronomy

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The segmentation and positioning of tea buds are the basis for intelligent picking robots to pick tea buds accurately. Tea images were collected in a complex environment, and median filtering was carried out to obtain tea bud images with smooth edges. Four semantic segmentation algorithms, U-Net, high-resolution network (HRNet_W18), fast semantic segmentation network (Fast-SCNN), and Deeplabv3+, were selected for processing images. The centroid of the tea buds and the image center of the minimum external rectangle were calculated. The farthest point from the centroid was extracted from the tea stalk orientation, which was the final picking point for tea buds. The experimental results showed that the mean intersection over union (mIoU) of HRNet_W18 was 0.81, and for a kernel with a median filter size of 3 × 3, the proportion of abnormal tea buds was only 11.6%. The average prediction accuracy of picking points with different tea stalk orientations was 57%. This study proposed a fresh tea bud segmentation and picking point location method based on a high-resolution network model. In addition, the cloud platform can be used for data sharing and real-time calculation of tea bud coordinates, reducing the computational burden of picking robots.

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An improved model based on YOLOX for detection of tea sprouts in natural environment

Li,

Liu,

et al. 2024

Evolving Systems

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Fresh Tea Sprouts Detection via Image Enhancement and Fusion SSD

Cited by 9 publications

References 26 publications

Tea Bud Detection and 3D Pose Estimation in the Field with a Depth Camera Based on Improved YOLOv5 and the Optimal Pose-Vertices Search Method

Tea Bud Detection and 3D Pose Estimation in the Field with a Depth Camera Based on Improved YOLOv5 and the Optimal Pose-Vertices Search Method

A Machine Vision-Based Method for Tea Buds Segmentation and Picking Point Location Used on a Cloud Platform

An improved model based on YOLOX for detection of tea sprouts in natural environment

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