Real-Time Detection of Vine Trunk for Robot Localization Using Deep Learning Models Developed for Edge TPU Devices

Alibabaei, Khadijeh; Assunção, Eduardo; Gaspar, Pedro Dinis; Soares, Vasco N. G. J.; Caldeira, João M. L. P.

doi:10.3390/fi14070199

Cited by 9 publications

(3 citation statements)

References 32 publications

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“…As TPUs evolve, they are expected to unlock new possibilities in AI, potentially leading to more advanced, efficient, and accessible AI applications across various sectors. This trajectory underscores the growing significance of TPUs as a central component in the rapidly advancing field of AI and ML, heralding a new era of computational capability and innovation [86][87][88].…”

Section: Tensor Processing Units (Tpus)mentioning

confidence: 99%

Advancements in Artificial Intelligence Circuits and Systems (AICAS)

Miller,

Durlik,

Kostecka

et al. 2023

Electronics

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In the rapidly evolving landscape of electronics, Artificial Intelligence Circuits and Systems (AICAS) stand out as a groundbreaking frontier. This review provides an exhaustive examination of the advancements in AICAS, tracing its development from inception to its modern-day applications. Beginning with the foundational principles that underpin AICAS, we delve into the state-of-the-art architectures and design paradigms that are propelling the field forward. This review also sheds light on the multifaceted applications of AICAS, from optimizing energy efficiency in electronic devices to empowering next-generation cognitive computing systems. Key challenges, such as scalability and robustness, are discussed in depth, along with potential solutions and emerging trends that promise to shape the future of AICAS. By offering a comprehensive overview of the current state and potential trajectory of AICAS, this review serves as a valuable resource for researchers, engineers, and industry professionals looking to harness the power of AI in electronics.

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Section: Tensor Processing Units (Tpus)mentioning

confidence: 99%

Advancements in Artificial Intelligence Circuits and Systems (AICAS)

Miller,

Durlik,

Kostecka

et al. 2023

Electronics

View full text Add to dashboard Cite

show abstract

“…LRR algorithm, for example, is used to de-noise images of cyclic spectrums before AMC models are implemented [27]. It is also possible to implement de-noising signals by implementing image de-noising algorithms [28], [29].…”

Section: Introductionmentioning

confidence: 99%

Robust Automatic Modulation Classification in Low Signal to Noise Ratio

Lee

2023

IEEE Access

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In a non-cooperative communication environment, automatic modulation classification (AMC) is an essential technology for analyzing signals and classifying different kinds of signal modulation before they are demodulated. Deep learning (DL)-based AMC has been proposed as an efficient method of achieving high classification performance. However, most current DL-AMC methods have limited generalization capabilities under varying noise conditions, especially at low signal-to-noise ratios (SNRs). Therefore, these methods can not be directly applied to practical systems. In this paper, we propose a threshold autoencoder denoiser convolutional neural network (TADCNN), which consists of a threshold autoencoder denoiser (TAD) and a convolutional neural network (CNN). TADs reduce noise power and clean input signals, which are then passed on to CNN for classification. The TAD network generally consists of three components: the batch normalization layer, the autoencoder, and the threshold denoise. The threshold denoise component uses an auto-learning threshold sub-network to compute thresholds automatically. According to experiments, AMC with TAD improved classification accuracy by 70% at low SNR compared with a model without a denoiser. Additionally, our model achieves an average accuracy of 66.64% on the RML2016.10A dataset, which is 6% to 18% higher than the current AMC model.

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“…The most significant challenges that any crop faces are diseases [1], pests [2], weeds [3], and nutritional deficiencies. Among them, identifying plant diseases through an optical analysis of disease signs on plant leaves presents a significant challenge.…”

Section: Introductionmentioning

confidence: 99%

A High-Precision Plant Disease Detection Method Based on a Dynamic Pruning Gate Friendly to Low-Computing Platforms

Liu

Cheng

et al. 2023

Plants

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Timely and accurate detection of plant diseases is a crucial research topic. A dynamic-pruning-based method for automatic detection of plant diseases in low-computing situations is proposed. The main contributions of this research work include the following: (1) the collection of datasets for four crops with a total of 12 diseases over a three-year history; (2) the proposition of a re-parameterization method to improve the boosting accuracy of convolutional neural networks; (3) the introduction of a dynamic pruning gate to dynamically control the network structure, enabling operation on hardware platforms with widely varying computational power; (4) the implementation of the theoretical model based on this paper and the development of the associated application. Experimental results demonstrate that the model can run on various computing platforms, including high-performance GPU platforms and low-power mobile terminal platforms, with an inference speed of 58 FPS, outperforming other mainstream models. In terms of model accuracy, subclasses with a low detection accuracy are enhanced through data augmentation and validated by ablation experiments. The model ultimately achieves an accuracy of 0.94.

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Real-Time Detection of Vine Trunk for Robot Localization Using Deep Learning Models Developed for Edge TPU Devices

Cited by 9 publications

References 32 publications

Advancements in Artificial Intelligence Circuits and Systems (AICAS)

Advancements in Artificial Intelligence Circuits and Systems (AICAS)

Robust Automatic Modulation Classification in Low Signal to Noise Ratio

A High-Precision Plant Disease Detection Method Based on a Dynamic Pruning Gate Friendly to Low-Computing Platforms

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