Congyue Wang
6 Papers
Congyue Wang is an academic researcher. The author has contributed to research in topics: Computer science & Ground-penetrating radar. The author has co-authored 3 publications.
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Papers
A Lightweight Cherry Tomato Maturity Real-Time Detection Algorithm Based on Improved YOLOV5n
TL;DR: The improved model presented in this paper enables real-time target recognition and maturity detection for cherry tomatoes and provides rapid and accurate target recognition guidance for achieving mechanical automatic picking of cherry tomatoes.
22
Detection of the foreign object positions in agricultural soils using Mask-RCNN
TL;DR: Li et al. as mentioned in this paper used Mask Region-based Convolutional Neural Network (Mask-RCNN) and a geometric model to improve the GPR positioning accuracy, and the results showed that pixel-level segmentation and positioning based on Mask RCNN can improve the accuracy of the position detection of objects in agricultural soil effectively.
10
Point Cloud Completion of Plant Leaves under Occlusion Conditions Based on Deep Learning
Haibo Chen,Shengbo Liu,Congyue Wang,Chaofeng Wang,Kangye Gong,Yuanhong Li,Yubin Lan +6 more
TL;DR: The accuracy of estimating the phenotypic parameters has been enhanced significantly, enabling efficient retrieval of such parameters in 3D plant reconstruction and a fresh perspective for non-destructive identification of plant phenotypes.
10
Interference Cancellation Scheme Based on Network Coding in Blockchain-Enabled Internet of Things
Congyue Wang,Baofeng Ji,Da Li +2 more
- 24 May 2023
TL;DR: In this paper , the authors compared and analyzed the performance of two-slot and three-slot network coding enhanced systems, and designed a new MAC layer protocol for blockchain-enabled IoT systems based on network coding technology.
An Intelligent Detection Method for Obstacles in Agricultural Soil with FDTD Modeling and MSVMs
TL;DR: In this paper , the authors used finite-difference time-domain (FDTD) simulated models to gather training data and predict actual soil conditions, and proposed a multi-class support vector machine (MSVM) that employs a semi-supervised algorithm to classify buried object materials and locate their position in soil.