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Monocular Depth Estimation with Self-Supervised Learning for Vineyard Unmanned Agricultural Vehicle
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In: Sensors (Basel) (2022)
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Abstract:
To find an economical solution to infer the depth of the surrounding environment of unmanned agricultural vehicles (UAV), a lightweight depth estimation model called MonoDA based on a convolutional neural network is proposed. A series of sequential frames from monocular videos are used to train the model. The model is composed of two subnetworks—the depth estimation subnetwork and the pose estimation subnetwork. The former is a modified version of U-Net that reduces the number of bridges, while the latter takes EfficientNet-B0 as its backbone network to extract the features of sequential frames and predict the pose transformation relations between the frames. The self-supervised strategy is adopted during the training, which means the depth information labels of frames are not needed. Instead, the adjacent frames in the image sequence and the reprojection relation of the pose are used to train the model. Subnetworks’ outputs (depth map and pose relation) are used to reconstruct the input frame, then a self-supervised loss between the reconstructed input and the original input is calculated. Finally, the loss is employed to update the parameters of the two subnetworks through the backward pass. Several experiments are conducted to evaluate the model’s performance, and the results show that MonoDA has competitive accuracy over the KITTI raw dataset as well as our vineyard dataset. Besides, our method also possessed the advantage of non-sensitivity to color. On the computing platform of our UAV’s environment perceptual system NVIDIA JETSON TX2, the model could run at 18.92 FPS. To sum up, our approach provides an economical solution for depth estimation by using monocular cameras, which achieves a good trade-off between accuracy and speed and can be used as a novel auxiliary depth detection paradigm for UAVs.
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Keyword:
Article
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URL: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8838921/ http://www.ncbi.nlm.nih.gov/pubmed/35161463 https://doi.org/10.3390/s22030721
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Translational regulation of Chk1 expression by eIF3a via interaction with the RNA-binding protein HuR
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In: Biochem J (2020)
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Lexical richness of Chinese candidates in the graded oral English examinations
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Find or Classify? Dual Strategy for Slot-Value Predictions on Multi-Domain Dialog State Tracking ...
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Additional file 1: of Estimating the prevalence of schistosomiasis japonica in China: a serological approach ...
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Additional file 1: of Estimating the prevalence of schistosomiasis japonica in China: a serological approach ...
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Alkali-silica reaction in waterglass-activated slag mortars incorporating fly ash and metakaolin
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Domain adaptation for statistical machine translation and neural machine translation
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Zhang, Jian. - : Dublin City University. School of Computing, 2017. : Dublin City University. ADAPT, 2017
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In: Zhang, Jian orcid:0000-0001-5659-5865 (2017) Domain adaptation for statistical machine translation and neural machine translation. PhD thesis, Dublin City University. (2017)
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Fast gated neural domain adaptation: language model as a case study
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In: Zhang, Jian orcid:0000-0001-5659-5865 , Wu, Xiaofeng, Way, Andy orcid:0000-0001-5736-5930 and Liu, Qun orcid:0000-0002-7000-1792 (2017) Fast gated neural domain adaptation: language model as a case study. In: Proceedings of FETLT 2016: Future and Emerging Trends in Language Technologies, Machine Learning and Big Data, 30 Nov- 2 Dec 2016, Seville, Spain. (2017)
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Refining Image Categorization by Exploiting Web Images and General Corpus ...
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The global view of mRNA-related ceRNA cross-talks across cardiovascular diseases
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Two-Stage Friend Recommendation Based on Network Alignment and Series Expansion of Probabilistic Topic Model
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In: Faculty of Engineering and Information Sciences - Papers: Part B (2017)
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Topic-informed neural machine translation
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In: Zhang, Jian, Li, Liangyou orcid:0000-0002-0279-003X , Way, Andy orcid:0000-0001-5736-5930 and Liu, Qun orcid:0000-0002-7000-1792 (2016) Topic-informed neural machine translation. In: 26th International Conference on Computational Linguistics, 13-16 Dec 2016, Osaka, Japan. (2016)
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Chinese Relative Clauses Processing in Supportive Context Removing Ambiguity
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In: Studies in Literature and Language; Vol 1, No 4 (2010): Studies in Literature and Language; 12-19 ; 1923-1563 ; 1923-1555 (2010)
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