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Mr. Renjie Xu
Nanjing Forestry University, No.159 Longpan Road, Xuanwu District, Nanjing City, Jiangsu Province, China

Basic Info

Basic Info is private.

Research Keywords & Expertise

0 Computer Vision
0 Deep Learning
0 Machine Learning
0 Remote Sensing
0 compressed sensing

Honors and Awards

Second Prize in "HUAWEI Cup" the 16th China Post-Graduate Mathematical Contest in Modeling

Organizing Committee of China Postgraduate Mathematical Contest in Modeling




Career Timeline

Nanjing Forestry University

Graduate Student or Post Graduate

01 September 2018 - 01 February 2021


Nanjing Forestry University

Undergraduate Student

01 September 2014 - 01 June 2018




Short Biography

Renjie Xu received the B.E. degree from the College of Information Science and Technology, Nanjing Forestry University, in 2018. He is currently pursuing the master's degree with the Nanjing Forestry University. His research interests include machine learning and computer vision.

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Journal article
Published: 13 February 2021 in Forests
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Due to the various shapes, textures, and colors of fires, forest fire detection is a challenging task. The traditional image processing method relies heavily on manmade features, which is not universally applicable to all forest scenarios. In order to solve this problem, the deep learning technology is applied to learn and extract features of forest fires adaptively. However, the limited learning and perception ability of individual learners is not sufficient to make them perform well in complex tasks. Furthermore, learners tend to focus too much on local information, namely ground truth, but ignore global information, which may lead to false positives. In this paper, a novel ensemble learning method is proposed to detect forest fires in different scenarios. Firstly, two individual learners Yolov5 and EfficientDet are integrated to accomplish fire detection process. Secondly, another individual learner EfficientNet is responsible for learning global information to avoid false positives. Finally, detection results are made based on the decisions of three learners. Experiments on our dataset show that the proposed method improves detection performance by 2.5% to 10.9%, and decreases false positives by 51.3%, without any extra latency.

ACS Style

Renjie Xu; Haifeng Lin; Kangjie Lu; Lin Cao; Yunfei Liu. A Forest Fire Detection System Based on Ensemble Learning. Forests 2021, 12, 217 .

AMA Style

Renjie Xu, Haifeng Lin, Kangjie Lu, Lin Cao, Yunfei Liu. A Forest Fire Detection System Based on Ensemble Learning. Forests. 2021; 12 (2):217.

Chicago/Turabian Style

Renjie Xu; Haifeng Lin; Kangjie Lu; Lin Cao; Yunfei Liu. 2021. "A Forest Fire Detection System Based on Ensemble Learning." Forests 12, no. 2: 217.

Journal article
Published: 26 February 2020 in Forests
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The terrestrial laser scanner (TLS) has been widely used in forest inventories. However, with increasing precision of TLS, storing and transmitting tree point clouds become more challenging. In this paper, a novel compressed sensing (CS) scheme for broad-leaved tree point clouds is proposed by analyzing and comparing different sparse bases, observation matrices, and reconstruction algorithms. Our scheme starts by eliminating outliers and simplifying point clouds with statistical filtering and voxel filtering. The scheme then applies Haar sparse basis to thin the coordinate data based on the characteristics of the broad-leaved tree point clouds. An observation procedure down-samples the point clouds with the partial Fourier matrix. The regularized orthogonal matching pursuit algorithm (ROMP) finally reconstructs the original point clouds. The experimental results illustrate that the proposed scheme can preserve morphological attributes of the broad-leaved tree within a range of relative error: 0.0010%–3.3937%, and robustly extend to plot-level within a range of mean square error (MSE): 0.0063–0.2245.

ACS Style

Renjie Xu; Ting Yun; Lin Cao; Yunfei Liu. Compression and Recovery of 3D Broad-Leaved Tree Point Clouds Based on Compressed Sensing. Forests 2020, 11, 257 .

AMA Style

Renjie Xu, Ting Yun, Lin Cao, Yunfei Liu. Compression and Recovery of 3D Broad-Leaved Tree Point Clouds Based on Compressed Sensing. Forests. 2020; 11 (3):257.

Chicago/Turabian Style

Renjie Xu; Ting Yun; Lin Cao; Yunfei Liu. 2020. "Compression and Recovery of 3D Broad-Leaved Tree Point Clouds Based on Compressed Sensing." Forests 11, no. 3: 257.