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Akademický článek

Arbitrary Oriented Few-Shot Object Detection in Remote Sensing Images

Autor: Wei Wu, Chengeng Jiang, Liao Yang, Weisheng Wang, Quanjun Chen, Junjian Zhang, Haiping Yang, Zuohui Chen

Publikováno v: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 17930-17944 (2024)

Few-shot object detection (FSOD) aims to identify novel objects using only a limited number of samples. While impressive results have been achieved in natural scene images, object detection in remote sensing images (RSIs) presents unique challenges d

Externí odkaz: https://doaj.org/article/d5e7db0561884d218f207bbe94e3194f

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Akademický článek

ResNet incorporating the fusion data of RGB & hyperspectral images improves classification accuracy of vegetable soybean freshness

Autor: Yuanpeng Bu, Jinxuan Hu, Cheng Chen, Songhang Bai, Zuohui Chen, Tianyu Hu, Guwen Zhang, Na Liu, Chang Cai, Yuhao Li, Qi Xuan, Ye Wang, Zhongjing Su, Yun Xiang, Yaming Gong

Publikováno v: Scientific Reports, Vol 14, Iss 1, Pp 1-13 (2024)

Abstract The freshness of vegetable soybean (VS) is an important indicator for quality evaluation. Currently, deep learning-based image recognition technology provides a fast, efficient, and low-cost method for analyzing the freshness of food. The RG

Externí odkaz: https://doaj.org/article/470181892ac04253b8f1238d0601b025

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Akademický článek

FTRNet: triplet fusion temporal relationship network for change detection in bitemporal remote sensing images

Autor: Wei Wu, Tong Li, Qi Xuan, QiMing Wan, Zuohui Chen

Publikováno v: Geocarto International, Vol 39, Iss 1 (2024)

Change detection (CD) in remote sensing (RS) images aims to identify surface changes based on images acquired at different times. However, existing methods are still unsatisfactory in locating fine details of change in RS images, due to overlooking t

Externí odkaz: https://doaj.org/article/2c16713875dc445491480cc18c1bb480

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Akademický článek

You Can’t Fool All the Models: Detect Adversarial Samples via Pruning Models

Autor: Renxuan Wang, Zuohui Chen, Hui Dong, Qi Xuan

Publikováno v: IEEE Access, Vol 9, Pp 163780-163790 (2021)

Many adversarial attack methods have investigated the security issue of deep learning models. Previous works on detecting adversarial samples show superior in accuracy but consume too much memory and computing resources. In this paper, we propose an

Externí odkaz: https://doaj.org/article/e75d3b9cbfeb4e41894ed37af18b1710

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Akademický článek

Deep Learning and Hyperspectral Images Based Tomato Soluble Solids Content and Firmness Estimation

Autor: Yun Xiang, Qijun Chen, Zhongjing Su, Lu Zhang, Zuohui Chen, Guozhi Zhou, Zhuping Yao, Qi Xuan, Yuan Cheng

Publikováno v: Frontiers in Plant Science, Vol 13 (2022)

Cherry tomato (Solanum lycopersicum) is popular with consumers over the world due to its special flavor. Soluble solids content (SSC) and firmness are two key metrics for evaluating the product qualities. In this work, we develop non-destructive test

Externí odkaz: https://doaj.org/article/2d6d0e493aae428fb3a07968002f3a72

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