scholarly journals The Automatic Detection of Pedestrians under the High-Density Conditions by Deep Learning Techniques

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Cheng-Jie Jin ◽  
Xiaomeng Shi ◽  
Ting Hui ◽  
Dawei Li ◽  
Ke Ma
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Automatic Detection ◽  
High Density ◽  
Video Data ◽  
Pedestrian Flow ◽  
Detection Model ◽  
Deep Learning Algorithm ◽  
Crowd Density Estimation ◽  
Flow Experiments

The automatic detection and tracking of pedestrians under high-density conditions is a challenging task for both computer vision fields and pedestrian flow studies. Collecting pedestrian data is a fundamental task for the modeling and practical implementations of crowd management. Although there are many methods for detecting pedestrians, they may not be easily adopted in the high-density situations. Therefore, we utilized one emerging method based on the deep learning algorithm. Based on the top-view video data of some pedestrian flow experiments recorded by an unmanned aerial vehicle (UAV), we produce our own training datasets. We train the detection model by using Yolo v3, a very popular deep learning model among many available detection models in recent years. We find the detection results are good; e.g., the precisions, recalls, and F1 scores could be larger than 0.95 even when the pedestrian density is as high as 9.0   ped / m 2 . We think this approach could be used for the other pedestrian flow experiments or field data which have similar configurations and can also be useful for automatic crowd density estimation.

scholarly journals Classification of Shellfish Recognition Based on Improved Faster R-CNN Framework of Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Yiran Feng ◽  
Xueheng Tao ◽  
Eung-Joo Lee
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Experimental Tests ◽  
Detection Algorithm ◽  
Detection Accuracy ◽  
Multiple Objects ◽  
Detection Model ◽  
Deep Learning Algorithm ◽  
Quality Sorting

In view of the current absence of any deep learning algorithm for shellfish identification in real contexts, an improved Faster R-CNN-based detection algorithm is proposed in this paper. It achieves multiobject recognition and localization through a second-order detection network and replaces the original feature extraction module with DenseNet, which can fuse multilevel feature information, increase network depth, and avoid the disappearance of network gradients. Meanwhile, the proposal merging strategy is improved with Soft-NMS, where an attenuation function is designed to replace the conventional NMS algorithm, thereby avoiding missed detection of adjacent or overlapping objects and enhancing the network detection accuracy under multiple objects. By constructing a real contexts shellfish dataset and conducting experimental tests on a vision recognition seafood sorting robot production line, we were able to detect the features of shellfish in different scenarios, and the detection accuracy was improved by nearly 4% compared to the original detection model, achieving a better detection accuracy. This provides favorable technical support for future quality sorting of seafood using the improved Faster R-CNN-based approach.

scholarly journals Automatic detection and tracking of marker seeds implanted in prostate cancer patients using a deep learning algorithm

2021 ◽  
Vol 46 (2) ◽  
pp. 80
Author(s):  
Prabhakar Ramachandran ◽  
Keya Amarsee ◽  
Andrew Fielding ◽  
Margot Lehman ◽  
Christopher Noble ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Deep Learning ◽  
Cancer Patients ◽  
Learning Algorithm ◽  
Automatic Detection ◽  
Deep Learning Algorithm ◽  
Detection And Tracking

scholarly journals Testing a deep learning algorithm for automatic detection of prenatal ultrasound for under-resourced communities

2022 ◽  
Vol 226 (1) ◽  
pp. S353-S354
Author(s):  
Marika Toscano ◽  
Junior Arroyo ◽  
Ana C. Saavedra ◽  
Thomas J. Marini ◽  
Timothy M. Baran ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Automatic Detection ◽  
Prenatal Ultrasound ◽  
Deep Learning Algorithm

scholarly journals Pattern Detection Model Using a Deep Learning Algorithm for Power Data Analysis in Abnormal Conditions

Electronics ◽  
2020 ◽  
Vol 9 (7) ◽  
pp. 1140
Author(s):  
Jeong-Hee Lee ◽  
Jongseok Kang ◽  
We Shim ◽  
Hyun-Sang Chung ◽  
Tae-Eung Sung
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Short Term Memory ◽  
Learning Algorithm ◽  
Pattern Detection ◽  
Prediction Algorithm ◽  
Data Set ◽  
Detection Model ◽  
Deep Learning Algorithm ◽  
Abnormal Pattern

Building a pattern detection model using a deep learning algorithm for data collected from manufacturing sites is an effective way for to perform decision-making and assess business feasibility for enterprises, by providing the results and implications of the patterns analysis of big data occurring at manufacturing sites. To identify the threshold of the abnormal pattern requires collaboration between data analysts and manufacturing process experts, but it is practically difficult and time-consuming. This paper suggests how to derive the threshold setting of the abnormal pattern without manual labelling by process experts, and offers a prediction algorithm to predict the potentials of future failures in advance by using the hybrid Convolutional Neural Networks (CNN)–Long Short-Term Memory (LSTM) algorithm, and the Fast Fourier Transform (FFT) technique. We found that it is easier to detect abnormal patterns that cannot be found in the existing time domain after preprocessing the data set through FFT. Our study shows that both train loss and test loss were well developed, with near zero convergence with the lowest loss rate compared to existing models such as LSTM. Our proposition for the model and our method of preprocessing the data greatly helps in understanding the abnormal pattern of unlabeled big data produced at the manufacturing site, and can be a strong foundation for detecting the threshold of the abnormal pattern of big data occurring at manufacturing sites.

scholarly journals Optimizing the Experimental Method for Stomata-Profiling Automation of Soybean Leaves Based on Deep Learning

Plants ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 2714
Author(s):  
Syada Nizer Sultana ◽  
Halim Park ◽  
Sung Hoon Choi ◽  
Hyun Jo ◽  
Jong Tae Song ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Imaging Data ◽  
Web Based ◽  
Detection Model ◽  
Deep Learning Algorithm ◽  
Soybean Leaves ◽  
Deep Learning Model ◽  
Clearing Method ◽  
Good Substitute

Stomatal observation and automatic stomatal detection are useful analyses of stomata for taxonomic, biological, physiological, and eco-physiological studies. We present a new clearing method for improved microscopic imaging of stomata in soybean followed by automated stomatal detection by deep learning. We tested eight clearing agent formulations based upon different ethanol and sodium hypochlorite (NaOCl) concentrations in order to improve the transparency in leaves. An optimal formulation—a 1:1 (v/v) mixture of 95% ethanol and NaOCl (6–14%)—produced better quality images of soybean stomata. Additionally, we evaluated fixatives and dehydrating agents and selected absolute ethanol for both fixation and dehydration. This is a good substitute for formaldehyde, which is more toxic to handle. Using imaging data from this clearing method, we developed an automatic stomatal detector using deep learning and improved a deep-learning algorithm that automatically analyzes stomata through an object detection model using YOLO. The YOLO deep-learning model successfully recognized stomata with high mAP (~0.99). A web-based interface is provided to apply the model of stomatal detection for any soybean data that makes use of the new clearing protocol.

scholarly journals Pill Detection Model for Medicine Inspection Based on Deep Learning

Chemosensors ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 4
Author(s):  
Hyuk-Ju Kwon ◽  
Hwi-Gang Kim ◽  
Sung-Hak Lee
Keyword(s):  
Deep Learning ◽  
Human Error ◽  
Learning Algorithm ◽  
Expansion Method ◽  
Training Data ◽  
Data Set ◽  
Learning Stage ◽  
Detection Model ◽  
Deep Learning Algorithm ◽  
Single Pill

This paper proposes a deep learning algorithm that can improve pill identification performance using limited training data. In general, when individual pills are detected in multiple pill images, the algorithm uses multiple pill images from the learning stage. However, when there is an increase in the number of pill types to be identified, the pill combinations in an image increase exponentially. To detect individual pills in an image that contains multiple pills, we first propose an effective database expansion method for a single pill. Then, the expanded training data are used to improve the detection performance. Our proposed method shows higher performance improvement than the existing algorithms despite the limited imaging and data set size. Our proposed method will help minimize problems, such as loss of productivity and human error, which occur while inspecting dispensed pills.

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