Low-cost whole slide imaging system withsingle-shot autofocusing based on colormultiplexed illumination and deep learning

In this paper, we proposed an approach to detect oilseed rape pests based on deep learning, which improves the mean average precision (mAP) to 77.14%; the result increased by 9.7% with the original model. We adopt this model to mobile platform to let every farmer able to use this program, which will diagnose pests in real time and provide suggestions on pest controlling. We designed an oilseed rape pest imaging database with 12 typical oilseed rape pests and compared the performance of five models, SSD w/Inception is chosen as the optimal model. Moreover, for the purpose of the high mAP, we have used data augmentation (DA) and added a dropout layer. The experiments are performed on the Android application we developed, and the result shows that our approach surpasses the original model obviously and is helpful for integrated pest management. This application has improved environmental adaptability, response speed, and accuracy by contrast with the past works and has the advantage of low cost and simple operation, which are suitable for the pest monitoring mission of drones and Internet of Things (IoT).

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A Low-Cost Embedded Facial Recognition System for Door Access Control using Deep Learning

2020 IEEE ANDESCON ◽

10.1109/andescon50619.2020.9271984 ◽

2020 ◽

Author(s):

Gustavo Orna ◽

Diego S. Benitez ◽

Noel Perez

Keyword(s):

Deep Learning ◽

Access Control ◽

Facial Recognition ◽

Low Cost ◽

Recognition System ◽

Facial Recognition System

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Computational Complexity Reduction of Neural Networks of Brain Tumor Image Segmentation by Introducing Fermi–Dirac Correction Functions

Entropy ◽

10.3390/e23020223 ◽

2021 ◽

Vol 23 (2) ◽

pp. 223

Author(s):

Yen-Ling Tai ◽

Shin-Jhe Huang ◽

Chien-Chang Chen ◽

Henry Horng-Shing Lu

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Computational Complexity ◽

High Performance ◽

Low Cost ◽

Structural Complexity ◽

Correction Function ◽

Computational Time ◽

Learning Methods ◽

Band Theory

Nowadays, deep learning methods with high structural complexity and flexibility inevitably lean on the computational capability of the hardware. A platform with high-performance GPUs and large amounts of memory could support neural networks having large numbers of layers and kernels. However, naively pursuing high-cost hardware would probably drag the technical development of deep learning methods. In the article, we thus establish a new preprocessing method to reduce the computational complexity of the neural networks. Inspired by the band theory of solids in physics, we map the image space into a noninteraction physical system isomorphically and then treat image voxels as particle-like clusters. Then, we reconstruct the Fermi–Dirac distribution to be a correction function for the normalization of the voxel intensity and as a filter of insignificant cluster components. The filtered clusters at the circumstance can delineate the morphological heterogeneity of the image voxels. We used the BraTS 2019 datasets and the dimensional fusion U-net for the algorithmic validation, and the proposed Fermi–Dirac correction function exhibited comparable performance to other employed preprocessing methods. By comparing to the conventional z-score normalization function and the Gamma correction function, the proposed algorithm can save at least 38% of computational time cost under a low-cost hardware architecture. Even though the correction function of global histogram equalization has the lowest computational time among the employed correction functions, the proposed Fermi–Dirac correction function exhibits better capabilities of image augmentation and segmentation.

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An Automated Light Trap to Monitor Moths (Lepidoptera) Using Computer Vision-Based Tracking and Deep Learning

Sensors ◽

10.3390/s21020343 ◽

2021 ◽

Vol 21 (2) ◽

pp. 343

Author(s):

Kim Bjerge ◽

Jakob Bonde Nielsen ◽

Martin Videbæk Sepstrup ◽

Flemming Helsing-Nielsen ◽

Toke Thomas Høye

Keyword(s):

Computer Vision ◽

Deep Learning ◽

Vision System ◽

Low Cost ◽

Light Trap ◽

Automatic Monitoring ◽

Light Sources ◽

Monitoring Methods ◽

Computer Vision System ◽

Substantial Investment

Insect monitoring methods are typically very time-consuming and involve substantial investment in species identification following manual trapping in the field. Insect traps are often only serviced weekly, resulting in low temporal resolution of the monitoring data, which hampers the ecological interpretation. This paper presents a portable computer vision system capable of attracting and detecting live insects. More specifically, the paper proposes detection and classification of species by recording images of live individuals attracted to a light trap. An Automated Moth Trap (AMT) with multiple light sources and a camera was designed to attract and monitor live insects during twilight and night hours. A computer vision algorithm referred to as Moth Classification and Counting (MCC), based on deep learning analysis of the captured images, tracked and counted the number of insects and identified moth species. Observations over 48 nights resulted in the capture of more than 250,000 images with an average of 5675 images per night. A customized convolutional neural network was trained on 2000 labeled images of live moths represented by eight different classes, achieving a high validation F1-score of 0.93. The algorithm measured an average classification and tracking F1-score of 0.71 and a tracking detection rate of 0.79. Overall, the proposed computer vision system and algorithm showed promising results as a low-cost solution for non-destructive and automatic monitoring of moths.

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A Survey on Deep Learning Based Methods and Datasets for Monocular 3D Object Detection

Electronics ◽

10.3390/electronics10040517 ◽

2021 ◽

Vol 10 (4) ◽

pp. 517

Author(s):

Seong-heum Kim ◽

Youngbae Hwang

Keyword(s):

Deep Learning ◽

Object Detection ◽

Low Cost ◽

Detection Methods ◽

Future Research ◽

3D Object ◽

Practical Applications ◽

Depth Sensors ◽

Significant Research ◽

3D Object Detection

Owing to recent advancements in deep learning methods and relevant databases, it is becoming increasingly easier to recognize 3D objects using only RGB images from single viewpoints. This study investigates the major breakthroughs and current progress in deep learning-based monocular 3D object detection. For relatively low-cost data acquisition systems without depth sensors or cameras at multiple viewpoints, we first consider existing databases with 2D RGB photos and their relevant attributes. Based on this simple sensor modality for practical applications, deep learning-based monocular 3D object detection methods that overcome significant research challenges are categorized and summarized. We present the key concepts and detailed descriptions of representative single-stage and multiple-stage detection solutions. In addition, we discuss the effectiveness of the detection models on their baseline benchmarks. Finally, we explore several directions for future research on monocular 3D object detection.

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Low-cost high-performance CMOS terahertz imaging system

2015 International SoC Design Conference (ISOCC) ◽

10.1109/isocc.2015.7401634 ◽

2015 ◽

Author(s):

Chung Hsing Li ◽

Tzu-Chao Yan ◽

Yuhsin Chang ◽

Chyong Chen ◽

Chien-Nan Kuo

Keyword(s):

High Performance ◽

Imaging System ◽

Low Cost ◽

Terahertz Imaging

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Synthetic polarization-sensitive optical coherence tomography by deep learning

npj Digital Medicine ◽

10.1038/s41746-021-00475-8 ◽

2021 ◽

Vol 4 (1) ◽

Author(s):

Yi Sun ◽

Jianfeng Wang ◽

Jindou Shi ◽

Stephen A. Boppart

Keyword(s):

Optical Coherence Tomography ◽

Deep Learning ◽

Imaging System ◽

Imaging Modality ◽

Similarity Index ◽

Polarization Sensitivity ◽

Imaging Method ◽

Optical Coherence ◽

The Real ◽

Oct Imaging

AbstractPolarization-sensitive optical coherence tomography (PS-OCT) is a high-resolution label-free optical biomedical imaging modality that is sensitive to the microstructural architecture in tissue that gives rise to form birefringence, such as collagen or muscle fibers. To enable polarization sensitivity in an OCT system, however, requires additional hardware and complexity. We developed a deep-learning method to synthesize PS-OCT images by training a generative adversarial network (GAN) on OCT intensity and PS-OCT images. The synthesis accuracy was first evaluated by the structural similarity index (SSIM) between the synthetic and real PS-OCT images. Furthermore, the effectiveness of the computational PS-OCT images was validated by separately training two image classifiers using the real and synthetic PS-OCT images for cancer/normal classification. The similar classification results of the two trained classifiers demonstrate that the predicted PS-OCT images can be potentially used interchangeably in cancer diagnosis applications. In addition, we applied the trained GAN models on OCT images collected from a separate OCT imaging system, and the synthetic PS-OCT images correlate well with the real PS-OCT image collected from the same sample sites using the PS-OCT imaging system. This computational PS-OCT imaging method has the potential to reduce the cost, complexity, and need for hardware-based PS-OCT imaging systems.

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