Analysis of Subsampled Image Size for Detection and Identification of Brake Pad Contours by Using Deep Learning

Drones are becoming increasingly popular not only for recreational purposes but in day-to-day applications in engineering, medicine, logistics, security and others. In addition to their useful applications, an alarming concern in regard to the physical infrastructure security, safety and privacy has arisen due to the potential of their use in malicious activities. To address this problem, we propose a novel solution that automates the drone detection and identification processes using a drone’s acoustic features with different deep learning algorithms. However, the lack of acoustic drone datasets hinders the ability to implement an effective solution. In this paper, we aim to fill this gap by introducing a hybrid drone acoustic dataset composed of recorded drone audio clips and artificially generated drone audio samples using a state-of-the-art deep learning technique known as the Generative Adversarial Network. Furthermore, we examine the effectiveness of using drone audio with different deep learning algorithms, namely, the Convolutional Neural Network, the Recurrent Neural Network and the Convolutional Recurrent Neural Network in drone detection and identification. Moreover, we investigate the impact of our proposed hybrid dataset in drone detection. Our findings prove the advantage of using deep learning techniques for drone detection and identification while confirming our hypothesis on the benefits of using the Generative Adversarial Networks to generate real-like drone audio clips with an aim of enhancing the detection of new and unfamiliar drones.

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Deep Learning for Automated Detection and Identification of Migrating American Eel Anguilla rostrata from Imaging Sonar Data

Remote Sensing ◽

10.3390/rs13142671 ◽

2021 ◽

Vol 13 (14) ◽

pp. 2671

Author(s):

Xiaoqin Zang ◽

Tianzhixi Yin ◽

Zhangshuan Hou ◽

Robert P. Mueller ◽

Zhiqun Daniel Deng ◽

...

Keyword(s):

Deep Learning ◽

Fish Passage ◽

Automated Detection ◽

Anguilla Rostrata ◽

Automated Method ◽

Detection And Identification ◽

And Migration ◽

Imaging Sonar ◽

American Eels ◽

Deep Learning Model

Adult American eels (Anguilla rostrata) are vulnerable to hydropower turbine mortality during outmigration from growth habitat in inland waters to the ocean where they spawn. Imaging sonar is a reliable and proven technology for monitoring of fish passage and migration; however, there is no efficient automated method for eel detection. We designed a deep learning model for automated detection of adult American eels from sonar data. The method employs convolution neural network (CNN) to distinguish between 14 images of eels and non-eel objects. Prior to image classification with CNN, background subtraction and wavelet denoising were applied to enhance sonar images. The CNN model was first trained and tested on data obtained from a laboratory experiment, which yielded overall accuracies of >98% for image-based classification. Then, the model was trained and tested on field data that were obtained near the Iroquois Dam located on the St. Lawrence River; the accuracy achieved was commensurate with that of human experts.

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Frontier: Autonomy in Detection, Actuation, and Planning for Robotic Weeding Systems

Transactions of the ASABE ◽

10.13031/trans.14085 ◽

2021 ◽

Vol 64 (2) ◽

pp. 557-563

Author(s):

Piyush Pandey ◽

Hemanth Narayan Dakshinamurthy ◽

Sierra N. Young

Keyword(s):

Artificial Intelligence ◽

Deep Learning ◽

Research And Development ◽

Weed Control ◽

List Type ◽

Automated Systems ◽

Weed Detection ◽

Detection And Identification ◽

Key Technologies ◽

Robotic Weeding

HighlightsRecent research and development efforts center around developing smaller, portable robotic weeding systems.Deep learning methods have resulted in accurate, fast, and robust weed detection and identification.Additional key technologies under development include precision actuation and multi-vehicle planning. Keywords: Artificial intelligence, Automated systems, Automated weeding, Weed control.

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FACE DETECTION AND IDENTIFICATION USING DEEP LEARNING TECHNOLOGY WITH CGAN AND SRGAN

10.15625/vap.2021.0046 ◽

2021 ◽

Author(s):

Phan Anh Cang ◽

To Huynh Thien Truong ◽

Cao Hùng Phi ◽

Phan Thuong Cang

Keyword(s):

Deep Learning ◽

Face Detection ◽

Learning Technology ◽

Detection And Identification

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Virus detection and identification in minutes using single-particle imaging and deep learning

10.1101/2020.10.13.20212035 ◽

2020 ◽

Author(s):

Nicolas Shiaelis ◽

Alexander Tometzki ◽

Leon Peto ◽

Andrew McMahon ◽

Christof Hepp ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

Single Particle ◽

Virus Detection ◽

Diagnostic Methods ◽

Clinical Samples ◽

Promising Alternative ◽

Detection And Identification ◽

Particle Imaging ◽

Single Particle Imaging

AbstractThe increasing frequency and magnitude of viral outbreaks in recent decades, epitomized by the current COVID-19 pandemic, has resulted in an urgent need for rapid and sensitive viral diagnostic methods. Here, we present a methodology for virus detection and identification that uses a convolutional neural network to distinguish between microscopy images of single intact particles of different viruses. Our assay achieves labeling, imaging and virus identification in less than five minutes and does not require any lysis, purification or amplification steps. The trained neural network was able to differentiate SARS-CoV-2 from negative clinical samples, as well as from other common respiratory pathogens such as influenza and seasonal human coronaviruses, with high accuracy. Single-particle imaging combined with deep learning offers a promising alternative to traditional viral diagnostic methods, and has the potential for significant impact.

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Adaptive Compression for Online Computer Vision: An Edge Reinforcement Learning Approach

ACM Transactions on Multimedia Computing Communications and Applications ◽

10.1145/3447878 ◽

2021 ◽

Vol 17 (4) ◽

pp. 1-23

Author(s):

Zhaoliang He ◽

Hongshan Li ◽

Zhi Wang ◽

Shutao Xia ◽

Wenwu Zhu

Keyword(s):

Computer Vision ◽

Deep Learning ◽

Reinforcement Learning ◽

Quality Level ◽

Image Size ◽

Standard Quality ◽

Encapsulation Method ◽

Cloud Servers ◽

Model Structures ◽

Deep Learning Model

With the growth of computer vision-based applications, an explosive amount of images have been uploaded to cloud servers that host such online computer vision algorithms, usually in the form of deep learning models. JPEG has been used as the de facto compression and encapsulation method for images. However, standard JPEG configuration does not always perform well for compressing images that are to be processed by a deep learning model—for example, the standard quality level of JPEG leads to 50% of size overhead (compared with the best quality level selection) on ImageNet under the same inference accuracy in popular computer vision models (e.g., InceptionNet and ResNet). Knowing this, designing a better JPEG configuration for online computer vision-based services is still extremely challenging. First, cloud-based computer vision models are usually a black box to end-users; thus, it is challenging to design JPEG configuration without knowing their model structures. Second, the “optimal” JPEG configuration is not fixed; instead, it is determined by confounding factors, including the characteristics of the input images and the model, the expected accuracy and image size, and so forth. In this article, we propose a reinforcement learning (RL)-based adaptive JPEG configuration framework, AdaCompress. In particular, we design an edge (i.e., user-side) RL agent that learns the optimal compression quality level to achieve an expected inference accuracy and upload image size, only from the online inference results, without knowing details of the model structures. Furthermore, we design an explore-exploit mechanism to let the framework fast switch an agent when it detects a performance degradation, mainly due to the input change (e.g., images captured across daytime and night). Our evaluation experiments using real-world online computer vision-based APIs from Amazon Rekognition, Face++, and Baidu Vision show that our approach outperforms existing baselines by reducing the size of images by one-half to one-third while the overall classification accuracy only decreases slightly. Meanwhile, AdaCompress adaptively re-trains or re-loads the RL agent promptly to maintain the performance.

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L2MXception: An Improved Xception Network for Classification of Peach diseases

10.21203/rs.3.rs-31469/v2 ◽

2020 ◽

Author(s):

Na Yao ◽

Fuchuan Ni ◽

Ziyan Wang ◽

Jun Luo ◽

Wing-Kin Sung ◽

...

Keyword(s):

Deep Learning ◽

Early Identification ◽

Image Data ◽

Yield Reduction ◽

Disease Prediction ◽

Imaging Data ◽

Regularization Term ◽

Detection And Identification ◽

L2 Norm

Abstract Background: Peach diseases can cause severe yield reduction and decreased quality for peach production. Rapid and accurate detection and identification of peach diseases is of great importance. Deep learning has been applied to detect peach diseases using imaging data. However, peach disease image data is difficult to collect and samples are imbalance. The popular deep networks perform poor for this issue.Results: This paper proposed an improved Xception network named as L2MXception which ensembles regularization term of L2-norm and mean. With the peach disease image dataset collected, results on seven mainstream deep learning models were compared in details and an improved loss function was integrated with regularization term L2-norm and mean (L2M Loss). Experiments showed that the Xception model with L2M Loss outperformed the current best method for peach disease prediction. Compared to the original Xception model, the validation accuracy of L2MXception was up to 93.85%, increased by 28.48%. Conclusions: The proposed L2MXception network may have great potential in early identification of peach diseases.

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