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Bin Li, Huazhong Lu, Xinyu Wei, Shixuan Guan, Zhenyu Zhang, Xingxing Zhou and Yizhi Luo
Accurate litchi identification is of great significance for orchard yield estimations. Litchi in natural scenes have large differences in scale and are occluded by leaves, reducing the accuracy of litchi detection models. Adopting traditional horizontal ...
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Yuntao Shi, Hongfei Zhang, Wei Guo, Meng Zhou, Shuqin Li, Jie Li and Yu Ding
This research proposes a face detection algorithm named LighterFace, which is aimed at enhancing detection speed to meet the demands of real-time community applications. Two pre-trained convolutional neural networks are combined, namely Cross Stage Parti...
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Ruidong Zhang and Xinguang Zhang
When using deep learning networks for dynamic feature rejection in SLAM systems, problems such as a priori static object motion leading to disturbed build quality and accuracy and slow system runtime are prone to occur. In this paper, based on the ORB-SL...
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Shuhe Zheng, Yang Liu, Wuxiong Weng, Xuexin Jia, Shilong Yu and Zuoxun Wu
Recognition and localization of fruits are key components to achieve automated fruit picking. However, current neural-network-based fruit recognition algorithms have disadvantages such as high complexity. Traditional stereo matching algorithms also have ...
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Wei Ji, Yu Pan, Bo Xu and Juncheng Wang
In order to enable the picking robot to detect and locate apples quickly and accurately in the orchard natural environment, we propose an apple object detection method based on Shufflenetv2-YOLOX. This method takes YOLOX-Tiny as the baseline and uses the...
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Seojin Jang, Wei Liu and Yongbeom Cho
Owing to their high accuracy, deep convolutional neural networks (CNNs) are extensively used. However, they are characterized by high complexity. Real-time performance and acceleration are required in current CNN systems. A graphics processing unit (GPU)...
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Jianwu Lin, Xiaoyulong Chen, Renyong Pan, Tengbao Cao, Jitong Cai, Yang Chen, Xishun Peng, Tomislav Cernava and Xin Zhang
Most convolutional neural network (CNN) models have various difficulties in identifying crop diseases owing to morphological and physiological changes in crop tissues, and cells. Furthermore, a single crop disease can show different symptoms. Usually, th...
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