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166 results on '"Xu, Yueqin"'

4. A Lightweight Detection Method for Blueberry Fruit Maturity Based on an Improved YOLOv5 Algorithm.

Author

Xiao, Feng, Wang, Haibin, Xu, Yueqin, and Shi, Zhen

Subjects
FRUIT, ALGORITHMS, OBJECT recognition (Computer vision), BLUEBERRIES, COMPUTER vision
Abstract

In order to achieve accurate, fast, and robust recognition of blueberry fruit maturity stages for edge devices such as orchard inspection robots, this research proposes a lightweight detection method based on an improved YOLOv5 algorithm. In the improved YOLOv5 algorithm, the ShuffleNet module is used to achieve lightweight deep-convolutional neural networks. The Convolutional Block Attention Module (CBAM) is also used to enhance the feature fusion capability of lightweight deep-convolutional neural networks. The effectiveness of this method is evaluated using the blueberry fruit dataset. The experimental results demonstrate that this method can effectively detect blueberry fruits and recognize their maturity stages in orchard environments. The average recall (R) of the detection is 92.0%. The mean average precision (mAP) of the detection at a threshold of 0.5 is 91.5%. The average speed of the detection is 67.1 frames per second (fps). Compared to other detection algorithms, such as YOLOv5, SSD, and Faster R-CNN, this method has a smaller model size, smaller network parameters, lower memory usage, lower computation usage, and faster detection speed while maintaining high detection performance. It is more suitable for migration and deployment on edge devices. This research can serve as a reference for the development of fruit detection systems for intelligent orchard devices. [ABSTRACT FROM AUTHOR]

Published
2024
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5. Fruit Detection and Recognition Based on Deep Learning for Automatic Harvesting: An Overview and Review.

Author

Xiao, Feng, Wang, Haibin, Xu, Yueqin, and Zhang, Ruiqing

Subjects
HARVESTING, DEEP learning, FRUIT, CONVOLUTIONAL neural networks, AGRICULTURE
Abstract

Continuing progress in machine learning (ML) has led to significant advancements in agricultural tasks. Due to its strong ability to extract high-dimensional features from fruit images, deep learning (DL) is widely used in fruit detection and automatic harvesting. Convolutional neural networks (CNN) in particular have demonstrated the ability to attain accuracy and speed levels comparable to those of humans in some fruit detection and automatic harvesting fields. This paper presents a comprehensive overview and review of fruit detection and recognition based on DL for automatic harvesting from 2018 up to now. We focus on the current challenges affecting fruit detection performance for automatic harvesting: the scarcity of high-quality fruit datasets, fruit detection of small targets, fruit detection in occluded and dense scenarios, fruit detection of multiple scales and multiple species, and lightweight fruit detection models. In response to these challenges, we propose feasible solutions and prospective future development trends. Future research should prioritize addressing these current challenges and improving the accuracy, speed, robustness, and generalization of fruit vision detection systems, while reducing the overall complexity and cost. This paper hopes to provide a reference for follow-up research in the field of fruit detection and recognition based on DL for automatic harvesting. [ABSTRACT FROM AUTHOR]

Published
2023
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45. Efficient heterojunction solar cells on p-type crystal silicon wafers

Author

Wang, Qi, primary, Page, M. R., additional, Iwaniczko, E., additional, Xu, Yueqin, additional, Roybal, L., additional, Bauer, R., additional, To, B., additional, Yuan, H.-C., additional, Duda, A., additional, Hasoon, F., additional, Yan, Y. F., additional, Levi, D., additional, Meier, D., additional, Branz, Howard M., additional, and Wang, T. H., additional

Published
2010
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