Research Archives - Page 5 of 10

10.03.2023.10.03.2023.

Developing Edge AI Computer Vision for Smart Poultry Farms Using Deep Learning and HPC

Researchers from the AIMHiGH team published a scientific paper in MDPI journal Sensors, This article belongs to the “Special Issue Novel Architectures and Applications for Artificial Intelligent and Internet of Things Ecosystems” (link). The paper “Developing Edge AI Computer Vision for Smart Poultry Farms Using Deep Learning and HPC” by S. Cakic, T. Popovic, S. Krco, D. Nedic, D. Babic, and I. Jovovic reports on the approach, experiences, and results of using HPC and AI to develop advanced Edge AI computer vision solutions for smart agriculture systems. AIMHiGH project is implemented as an experiment done in the context of FF4EuroHPC project. FF4EuroHPC is a European initiative that helps facilitate access to all high-performance computing-related technologies for SMEs and thus increases the innovation potential of European industry. Whether it is running high-resolution simulations, doing large-scale data analyses, or incorporating AI applications into SMEs’ workflows, FF4EuroHPC connects business with cutting-edge technologies. Learn more at: link.

ABSTRACT – This research describes the use of high-performance computing (HPC) and deep learning to create prediction models that could be deployed on edge AI devices equipped with camera and installed in poultry farms. The main idea is to leverage an existing IoT farming platform and use HPC offline to run deep learning to train the models for object detection and object segmentation, where the objects are chickens in images taken on farm. The models can be ported from HPC to edge AI devices to create a new type of computer vision kit to enhance the existing digital poultry farm platform. Such new sensors enable implementing functions such as counting chickens, detection of dead chickens, and even assessing their weight or detecting uneven growth. These functions combined with the monitoring of environmental parameters, could enable early disease detection and improve the decision-making process. The experiment focused on Faster R-CNN architectures and AutoML was used to identify the most suitable architecture for chicken detection and segmentation for the given dataset. For the selected architectures, further hyperparameter optimization was carried out and we achieved the accuracy of AP = 85%, AP50 = 98%, and AP75 = 96% for object detection and AP = 90%, AP50 = 98%, and AP75 = 96% for instance segmentation. These models were installed on edge AI devices and evaluated in the online mode on actual poultry farms. Initial results are promising, but further development of the dataset and improvements in prediction models is needed.

17.02.2023.19.02.2023.

IEEE IT2023: Disease Prediction Using ML Algorithms

Researchers from NCC Montenegro presented a paper at the 27th IEEE Conference on Information Technology IT2023 on 17th February 2023. The paper is titled “Disease Prediction Using Machine Learning Algorithms” and authored by I. Jovovic, D. Babic, T. Popovic, S. Cakic and I. Katnic.

ABSTRACT – This study aimed to investigate the application of machine learning techniques for disease prediction. Three popular machine learning algorithms, Random Forest, Support Vector Machines and Naive Bayes, were employed and their performance was evaluated. Results showed that the best performing model was based on Random Forest algorithm with the average accuracy of 87%. This model has been additionally tuned in order to achieve even better performance, which resulted with 90% accuracy. This study highlights the potential of AI in disease prediction and provides insights into the importance of algorithm selection and tuning for optimal performance.

Mr. Ivan Jovovic presenting at IEEE 2023 conference

The paper will soon be available through IEEE Xplore

17.02.2023.28.04.2023.

IEEE IT2023: Vision-based Vehicle Speed Estimation Using the YOLO Detector and RNN

Researchers from NCC Montenegro presented a paper at the 27th IEEE Conference on Information Technology IT2023. The paper is titled “Vision-based Vehicle Speed Estimation Using the YOLO Detector and RNN” and authored by Andrija Peruničić, Slobodan Djukanović and Andrej Cvijetić

ABSTRACT : The paper deals with vehicle speed estimation using video data obtained from a single camera. We propose a speed estimation method which uses the YOLO algorithm for vehicle detection and tracking, and a recurrent neural network (RNN) for speed estimation. As input features for speed estimation, we use the position and size of bounding boxes around the vehicles, extracted by the YOLO detector. The proposed method is trained and tested on the recently proposed VS13 dataset. The experimental results show that the box position does not bring any improvement in the speed estimation performance. The proposed RNN-based estimator gives an average error of 4.08 km/h using only the area of bounding box as input feature, which significantly outperforms audio-based approaches on the same dataset.

Link : https://ieeexplore.ieee.org/document/10078639

17.02.2023.28.04.2023.

IEEE IT2023: Deep learning-based vehicle speed estimation using the YOLO detector and 1D-CNN

Researchers from NCC Montenegro presented a paper at the 27th IEEE Conference on Information Technology IT2023. The paper is titled “Deep learning-based vehicle speed estimation using the YOLO detector and 1D-CNN” and authored by Andrej Cvijetić, Slobodan Djukanović and Andrija Peruničić

ABSTRACT : This paper addresses vehicle speed estimation using visual data obtained from a single video camera. The proposed method accurately predicts the speed of a vehicle, using the YOLO algorithm for vehicle detection and tracking, and a one-dimensional convolutional neural network (1D-CNN) for speed estimation. The YOLO algorithm outputs bounding boxes around detected objects in an image, which is, in our case, the vehicle whose speed is to be predicted. As input to our 1D-CNN speed estimation model, we introduce a novel feature based on the change of area of the bounding box around the vehicle. The feature, referred to as the changing bounding box area (CBBA), is obtained by calculating the area of the bounding box, frame-to-frame, as the vehicle approaches the camera. The shape of the CBBA curve remains closely the same for all vehicles, with differences conditioned by the value of the observed vehicle’s speed. The proposed method is trained and tested on the VS13 dataset. Experiments show that it is able to accurately predict the vehicle’s speed with an average error of 2.76 km/h, with the best performing vehicle having the average error of just 1.31 km/h. The proposed method exhibits the robustness as a key advantage, eliminating the need for prior knowledge of real-world dimensions such as the vehicle size, road width, camera distance and angle in relation to the road etc.

Link : https://ieeexplore.ieee.org/document/10078518

17.02.2023.28.04.2023.

IEEE IT2023: Vehicle Speed Estimation From Audio Signals Using 1D Convolutional Neural Networks

Researchers from NCC Montenegro presented a paper at the 27th IEEE Conference on Information Technology IT2023.

The paper is titled “Vehicle Speed Estimation From Audio Signals Using 1D Convolutional Neural Networks” and authored by Ivana Čavor and Slobodan Djukanović.

ABSTRACT : This paper presents an approach to acoustic vehicle speed estimation using audio data obtained from single-sensor measurements. One-dimensional convolutional neural network (1D CNN) is used to estimate the vehicle’s speed directly from raw audio signal. The proposed approach does not require manual feature extraction and can be trained directly on unprocessed time-domain signals. The VS13 dataset, which contains 400 audio-video recordings of 13 different vehicles, is used for training and testing of the proposed model. Two training procedures have been evaluated and tested, one based on determining optimal number of training epochs and the other based on recording model state with minimal validation loss. The experimental results show that the average estimation error on VS13 is 9.50 km/h and 8.88 km/h, respectively.

Link : https://ieeexplore.ieee.org/document/10078724

15.02.2023.28.02.2023.

HPC4S3ME project presented at the IEEE IT2023

New reearch project HPC4S3ME was presented by prof. Tomo Popovic and dr. Luka Filipovic at the 27th IEEE International Conference – Information Technology – IT2023. The conference had a special session dedicated to presentation of project results for the projects implemented in Montenegro and the region. The conference program is available at the following link.

The full title of this new project is “Building scientific and innovation potential to utilize HPC and AI for S3 Smart Specialisation in Montenegro – HPC4S3ME” and it is funded by the through IPA II program, call reference EuropeAid/172-351/ID/ACT/ME.

Proposing the use of HPC for research in Montenegrin S3 priority domains

Project was presented by prof. Popovic and dr. Filipovic

The overall objective of HPC4S3ME project is to contribute to straightening research excellence by building scientific and innovation potential based on the use of high performance computing (HPC) and artificial intelligence (AI) for applications in industrial domains proposed by the Smart Specialisation Strategy (2019-2024) for Montenegro. The implementation of this project will provide a state-of-the-art environment for young researchers to gain experience in research and development in computer science, more specifically to apply machine learning (ML) and deep learning (DL) algorithms supported by HPC in order to create innovative information-communication technology (ICT) solutions for applications in agriculture and food value chain, health and tourism, energy and sustainable environment., i.e. priority domains identified by the smart specialisation strategy. More information on the project available at the HPC4S3ME website.

HPC4S3ME focuses on HPC capacity building and support for young researchers

28.01.2023.28.04.2023.

Journal Technology and Health Care: Verification of temperature, wind and precipitation fields for the high-resolution WRF NMM model over the complex terrain of Montenegro

Researchers from NCC Montenegro presented a paper at the “Special Issue for magazine Technology and health care: official journal of the European Society for Engineering and Medicine”. The paper titled “Verification of temperature, wind and precipitation fields for the high-resolution WRF NMM model over the complex terrain of Montenegro” is written by Zečević Aleksandar, Filipović Luka and Marčev Angel.

ABSTRACT :

BACKGROUND: The necessity of setting up high-resolution models is essential to timely forecast dangerous meteorological phenomena.

OBJECTIVE: This study presents a verification of the numerical Weather Research and Forecasting non-hydrostatic Mesoscale Model (WRF NMM) for weather prediction using the High-Performance Computing (HPC) cluster over the complex relief of Montenegro.

METHODS: Verification was performed comparing WRF NMM predicted values and measured values for temperature, wind and precipitation for six Montenegrin weather stations in a five-year period using statistical parameters. The difficult task of adjusting the model over the complex Montenegrin terrain is caused by a rapid altitude change in in the coastal area, numerous karst fields, basins, river valleys and canyons, large areas of artificial lakes on a relatively small terrain.

RESULTS: Based on the obtained verification results, the results of the model vary during time of day, the season of the year, the altitude of the station for which the model results were verified, as well as the surrounding relief for them. The results show the best performance in the central region and show deviations for some metrological measures in some periods of the year.

CONCLUSION: This study can give recommendations on how to adapt a numerical model to a real situation in order to produce better weather forecast for the public.

Link : https://content.iospress.com/articles/technology-and-health-care/thc229016