scholarly journals Multi-Saliency Map and Machine Learning Based Human Detection for the Embedded Top-View Imaging System

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Seung Jun Lee ◽  
Byeong Hak Kim ◽  
Min Young Kim
Keyword(s):  
Machine Learning ◽  
Imaging System ◽  
Saliency Map ◽  
Human Detection ◽  
Top View

scholarly journals Machine-learning-based top-view safety monitoring of ground workforce on complex industrial sites

2021 ◽  
Author(s):  
Gelayol Golcarenarenji ◽  
Ignacio Martinez-Alpiste ◽  
Qi Wang ◽  
Jose Maria Alcaraz-Calero
Keyword(s):  
Machine Learning ◽  
Health And Safety ◽  
Human Detection ◽  
Industrial Sites ◽  
Machine Learning Model ◽  
D Modules ◽  
Level Information ◽  
Time Requirements ◽  
Spatial Pyramid Pooling ◽  
Top View

AbstractTelescopic cranes are powerful lifting facilities employed in construction, transportation, manufacturing and other industries. Since the ground workforce cannot be aware of their surrounding environment during the current crane operations in busy and complex sites, accidents and even fatalities are not avoidable. Hence, deploying an automatic and accurate top-view human detection solution would make significant improvements to the health and safety of the workforce on such industrial operational sites. The proposed method (CraneNet) is a new machine learning empowered solution to increase the visibility of a crane operator in complex industrial operational environments while addressing the challenges of human detection from top-view on a resource-constrained small-form PC to meet the space constraint in the operator’s cabin. CraneNet consists of 4 modified ResBlock-D modules to fulfill the real-time requirements. To increase the accuracy of small humans at high altitudes which is crucial for this use-case, a PAN (Path Aggregation Network) was designed and added to the architecture. This enhances the structure of CraneNet by adding a bottom-up path to spread the low-level information. Furthermore, three output layers were employed in CraneNet to further improve the accuracy of small objects. Spatial Pyramid Pooling (SPP) was integrated at the end of the backbone stage which increases the receptive field of the backbone, thereby increasing the accuracy. The CraneNet has achieved 92.59% of accuracy at 19 FPS on a portable device. The proposed machine learning model has been trained with the Standford Drone Dataset and Visdrone 2019 to further show the efficacy of the smart crane approach. Consequently, the proposed system is able to detect people in complex industrial operational areas from a distance up to 50 meters between the camera and the person. This system is also applicable to the detection of any other objects from an overhead camera.

scholarly journals Precise Estimation of NDVI with a Simple NIR Sensitive RGB Camera and Machine Learning Methods for Corn Plants

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3208 ◽  
Author(s):  
Liangju Wang ◽  
Yunhong Duan ◽  
Libo Zhang ◽  
Tanzeel U. Rehman ◽  
Dongdong Ma ◽  
...  
Keyword(s):  
Machine Learning ◽  
Professional Training ◽  
Imaging System ◽  
Estimation Method ◽  
Machine Learning Algorithms ◽  
Plant Phenotyping ◽  
Band Pass Filter ◽  
Pass Filter ◽  
Corn Plants ◽  
Hyperspectral Camera

The normalized difference vegetation index (NDVI) is widely used in remote sensing to monitor plant growth and chlorophyll levels. Usually, a multispectral camera (MSC) or hyperspectral camera (HSC) is required to obtain the near-infrared (NIR) and red bands for calculating NDVI. However, these cameras are expensive, heavy, difficult to geo-reference, and require professional training in imaging and data processing. On the other hand, the RGBN camera (NIR sensitive RGB camera, simply modified from standard RGB cameras by removing the NIR rejection filter) have also been explored to measure NDVI, but the results did not exactly match the NDVI from the MSC or HSC solutions. This study demonstrates an improved NDVI estimation method with an RGBN camera-based imaging system (Ncam) and machine learning algorithms. The Ncam consisted of an RGBN camera, a filter, and a microcontroller with a total cost of only $70 ~ 85. This new NDVI estimation solution was compared with a high-end hyperspectral camera in an experiment with corn plants under different nitrogen and water treatments. The results showed that the Ncam with two-band-pass filter achieved high performance (R2 = 0.96, RMSE = 0.0079) at estimating NDVI with the machine learning model. Additional tests showed that besides NDVI, this low-cost Ncam was also capable of predicting corn plant nitrogen contents precisely. Thus, Ncam is a potential option for MSC and HSC in plant phenotyping projects.

scholarly journals Pain assessment in horses using automatic facial expression recognition through deep learning-based modeling

PLoS ONE ◽  
2021 ◽  
Vol 16 (10) ◽  
pp. e0258672
Author(s):  
Gabriel Carreira Lencioni ◽  
Rafael Vieira de Sousa ◽  
Edson José de Souza Sardinha ◽  
Rodrigo Romero Corrêa ◽  
Adroaldo José Zanella
Keyword(s):  
Machine Learning ◽  
Machine Vision ◽  
Facial Expressions ◽  
Evaluation System ◽  
Imaging System ◽  
Daily Routine ◽  
Human Observer ◽  
Expression Recognition ◽  
Efficient Treatment ◽  
Vision Algorithm

The aim of this study was to develop and evaluate a machine vision algorithm to assess the pain level in horses, using an automatic computational classifier based on the Horse Grimace Scale (HGS) and trained by machine learning method. The use of the Horse Grimace Scale is dependent on a human observer, who most of the time does not have availability to evaluate the animal for long periods and must also be well trained in order to apply the evaluation system correctly. In addition, even with adequate training, the presence of an unknown person near an animal in pain can result in behavioral changes, making the evaluation more complex. As a possible solution, the automatic video-imaging system will be able to monitor pain responses in horses more accurately and in real-time, and thus allow an earlier diagnosis and more efficient treatment for the affected animals. This study is based on assessment of facial expressions of 7 horses that underwent castration, collected through a video system positioned on the top of the feeder station, capturing images at 4 distinct timepoints daily for two days before and four days after surgical castration. A labeling process was applied to build a pain facial image database and machine learning methods were used to train the computational pain classifier. The machine vision algorithm was developed through the training of a Convolutional Neural Network (CNN) that resulted in an overall accuracy of 75.8% while classifying pain on three levels: not present, moderately present, and obviously present. While classifying between two categories (pain not present and pain present) the overall accuracy reached 88.3%. Although there are some improvements to be made in order to use the system in a daily routine, the model appears promising and capable of measuring pain on images of horses automatically through facial expressions, collected from video images.

scholarly journals Detection of Masses in Mammograms using Bayesian Method and Machine learning

2018 ◽  
Vol 7 (4.6) ◽  
pp. 108
Author(s):  
Priyadarshini Chatterjee ◽  
Ch. Mamatha ◽  
T. Jagadeeswari ◽  
Katha Chandra Shekhar
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Bayesian Method ◽  
Learning Algorithm ◽  
Correct Diagnosis ◽  
Machine Learning Algorithms ◽  
Human Detection ◽  
Breast Masses ◽  
Learning Machine ◽  
Vast Area

Every 100th cases in cancer we come across are of breasts cancer cases. It is becoming very common in woman of all ages. Correct detection of these lesions in breast is very important. With less of human intervention, the goal is to do the correct diagnosis. Not all the cases of breast masses are futile. If the cases are not dealt properly, they might create panic amongst people. Human detection without machine intervention is not hundred percent accurate. If machines can be deeply trained, they can do the same work of detection with much more accuracy. Bayesian method has a vast area of application in the field of medical image processing as well as in machine learning. This paper intends to use Bayesian probabilistic in image segmentation as well as in machine learning. Machine learning in image processing means application in pattern recognition. There are various machine learning algorithms that can classify an image at their best. In the proposed system, we will be firstly segment the image using Bayesian method. On the segmented parts of the image, we will be applying machine learning algorithm to diagnose the mass or the growth.  

scholarly journals Computer Vision and Machine Learning Analysis of Commercial Rice Grains: A Potential Digital Approach for Consumer Perception Studies

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6354
Author(s):  
Aimi Aznan ◽  
Claudia Gonzalez Viejo ◽  
Alexis Pang ◽  
Sigfredo Fuentes
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Imaging System ◽  
Cost Effective ◽  
Consumer Perception ◽  
Rice Grain ◽  
Ann Model ◽  
Practical Applications ◽  
Rice Samples ◽  
Artificial Neural Network Ann

Rice quality assessment is essential for meeting high-quality standards and consumer demands. However, challenges remain in developing cost-effective and rapid techniques to assess commercial rice grain quality traits. This paper presents the application of computer vision (CV) and machine learning (ML) to classify commercial rice samples based on dimensionless morphometric parameters and color parameters extracted using CV algorithms from digital images obtained from a smartphone camera. The artificial neural network (ANN) model was developed using nine morpho-colorimetric parameters to classify rice samples into 15 commercial rice types. Furthermore, the ANN models were deployed and evaluated on a different imaging system to simulate their practical applications under different conditions. Results showed that the best classification accuracy was obtained using the Bayesian Regularization (BR) algorithm of the ANN with ten hidden neurons at 91.6% (MSE = <0.01) and 88.5% (MSE = 0.01) for the training and testing stages, respectively, with an overall accuracy of 90.7% (Model 2). Deployment also showed high accuracy (93.9%) in the classification of the rice samples. The adoption by the industry of rapid, reliable, and accurate methods, such as those presented here, may allow the incorporation of different morpho-colorimetric traits in rice with consumer perception studies.

scholarly journals Applying saliency-map analysis in searches for pulsars and fast radio bursts

2020 ◽  
Vol 642 ◽  
pp. A26
Author(s):  
C. Zhang ◽  
C. Wang ◽  
G. Hobbs ◽  
C. J. Russell ◽  
D. Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Simulated Data ◽  
Saliency Map ◽  
Machine Learning Algorithms ◽  
Dispersion Measure ◽  
Radio Bursts ◽  
Transient Event ◽  
Saliency Maps ◽  
Single Burst ◽  
Map Analysis

Context. We investigate the use of saliency-map analysis to aid in searches for transient signals, such as fast radio bursts and individual pulses from radio pulsars. Aims. Our aim is to demonstrate that saliency maps provide the means to understand predictions from machine learning algorithms and can be implemented in pipelines used to search for transient events. Methods. We implemented a new deep learning methodology to predict whether any segment of the data contains a transient event. The algorithm was trained using real and simulated data sets. We demonstrate that the algorithm is able to identify such events. The output results are visually analysed via the use of saliency maps. Results. We find that saliency maps can produce an enhanced image of any transient feature without the need for de-dispersion or removal of radio frequency interference. The maps can be used to understand which features in the image were used in making the machine learning decision and to visualise the transient event. Even though the algorithm reported here was developed to demonstrate saliency-map analysis, we have detected a single burst event, in archival data, with dispersion measure of 41 cm−3 pc that is not associated with any currently known pulsar.

scholarly journals Automatic Machine-Learning Classification of the Mode of Invasion of Oral Squamous Cell Carcinoma Using Digital Microscopic Images: A Retrospective Study

2020 ◽  
Author(s):  
Kunio Yoshizawa ◽  
Hidetoshi Ando ◽  
Yujiro Kimura ◽  
Shuichi Kawashiri ◽  
Akinori Moroi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Squamous Cell Carcinoma ◽  
Retrospective Study ◽  
Oral Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Imaging System ◽  
Accurate Determination ◽  
Automatic Machine ◽  
Machine Learning Classification

Abstract Background: The Yamamoto-Kohama criteria (YK), which are used to classify the morphology of the infiltrating protrusions of an oral squamous cell carcinoma (OSCC), are clinically useful for determining the mode of tumor invasion, especially in Japan. However, evaluations of the mode of OSCC invasion are based on subjective visual observations, and this approach has created considerable inter-evaluator and inter-facility differences. In this retrospective study, we aimed to develop an automatic method of determining the mode of invasion of OSCC based on the processing of digital medical images of the invasion front. Methods: Using 101 digitized photographic images of anonymized stained specimen slides from consecutive patients with OSCC at Kanazawa University, we created a classifier that allowed clinicians to introduce feature values and subjected the cases to machine learning using a random forest approach. We then compared the YK grades (1, 2, 3, 4C, 4D) determined by a human oral and maxillofacial surgeon with those determined using the machine learning approach. Results: The input of multiple test images into the newly created classifier yielded an overall F-measure value of 87%, (Grade 1: 93%, Grade 2: 67%, Grade 3: 89%, Grade 4C: 83%, Grade 4D: 94%). These results suggest that the output of the classifier was very similar to the judgments of the clinician. Conclusions: We successfully developed an automatic machine-learning method for discriminating the mode of invasion of OSCC. Our results suggest that a medical diagnostic imaging system could feasibly be used to provide an accurate determination of the mode of OSCC invasion.

scholarly journals Vision based Office Automation and Security System using Machine Learning and Internet of Things

2018 ◽  
Vol 7 (2.24) ◽  
pp. 42
Author(s):  
Amber Goel ◽  
Apaar Khurana ◽  
Pranav Sehgal ◽  
K Suganthi
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Face Recognition ◽  
Internet Of Things ◽  
Office Automation ◽  
Detection Algorithm ◽  
Machine Learning Algorithms ◽  
Human Detection ◽  
Raspberry Pi ◽  
Efficient Tool

The paper focuses on two areas, automation and security. Raspberry Pi is the heart of the project and it is fuelled by Machine Learning Algorithms using Open CV and Internet of Things. Face recognition uses Linear Binary Pattern and if an unknown person uses their workstation, a message will be sent to the respective person with the photo of the person who uses the workstation. Face recognition is also being used for uploading attendance and switching ON and OFF appliances automatically. During un-official hours, A Human Detection algorithm is being used to detect the human presence. If an unknown person enters the office, a photo of the person will be taken and sent to the authorities. This technology is a combination of Computer Vision, Machine learning and Internet of things, that serves to be an efficient tool for both automation and security.  

Sign in / Sign up

Export Citation Format

Share Document