| 1. |
- Alqaysi, Hiba, et al.
(författare)
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A temporal boosted yolo-based model for birds detection around wind farms
- 2021
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Ingår i: Journal of Imaging. - : MDPI AG. - 2313-433X. ; 7:11
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Tidskriftsartikel (refereegranskat)abstract
- Object detection for sky surveillance is a challenging problem due to having small objects in a large volume and a constantly changing background which requires high resolution frames. For example, detecting flying birds in wind farms to prevent their collision with the wind turbines. This paper proposes a YOLOv4-based ensemble model for bird detection in grayscale videos captured around wind turbines in wind farms. In order to tackle this problem, we introduce two datasets—(1) Klim and (2) Skagen—collected at two locations in Denmark. We use Klim training set to train three increasingly capable YOLOv4 based models. Model 1 uses YOLOv4 trained on the Klim dataset, Model 2 introduces tiling to improve small bird detection, and the last model uses tiling and temporal stacking and achieves the best mAP values on both Klim and Skagen datasets. We used this model to set up an ensemble detector, which further improves mAP values on both datasets. The three models achieve testing mAP values of 82%, 88%, and 90% on the Klim dataset. mAP values for Model 1 and Model 3 on the Skagen dataset are 60% and 92%. Improving object detection accuracy could mitigate birds’ mortality rate by choosing the locations for such establishment and the turbines location. It can also be used to improve the collision avoidance systems used in wind energy facilities.
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| 2. |
- Alqaysi, Hiba
(författare)
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Cost Optimization of Volumetric Surveillance for Sky Monitoring : Towards Flying Object Detection and Positioning
- 2022
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Unlike surface surveillance, volumetric monitoring deals with three-dimensional target space and moving objects within it. In sky monitoring, objects fly within outdoor and often remote volumes, such as wind farms and airport runways. Therefore, multiple cameras should be implemented to monitor these volumes and analyze flying activities.Due to that, challenges in designing and deploying volumetric surveillance systems for these applications arise. These include configuring the multi-camera node placement, coverage, cost, and the system's ability to detect and position flying objects.The research in this dissertation focuses on three aspects to optimize volumetric surveillance systems in sky monitoring applications. First, the node placement and coverage should be considered in accordance with the monitoring constraints. Also, the node architecture should be configured to minimize the design cost and maximize the coverage. Last, the system should detect small flying objects with good accuracy.Placing the multi-camera nodes in a hexagonal pattern while allowing overlap between adjacent nodes optimizes the placement. The inclusion of monitoring constraints like monitoring altitude and detection pixel resolution influences the node design. Furthermore, presented results show that modeling the multi-camera nodes as a cylinder rather than a hemisphere minimizes the cost of each node. The design exploration in this thesis provides a method to minimize the node cost based on defined design constraints. It also maximizes the coverage in terms of the number of square meters per dollar. Surveillance systems for sky monitoring should be able to detect and position flying objects. Therefore, two new annotated datasets were introduced that can be used for developing in-flight birds detection methods. The datasets were collected by Mid Sweden University at two locations in Denmark. A YOLOv4-based model for birds detection in 4k grayscale videos captured in wind farms is developed. The model overcomes the problem of detecting small objects in dynamic background, and it improves detection accuracy through tiling and temporal information incorporation, compared to the standard YOLOv4 and background subtraction.
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| 3. |
- Alqaysi, Hiba, et al.
(författare)
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Cost Optimized Design of Multi-Camera Domefor Volumetric Surveillance
- 2021
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Ingår i: IEEE Sensors Journal. - 1530-437X .- 1558-1748. ; 21:3, s. 3730-3737
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Tidskriftsartikel (refereegranskat)abstract
- A multi-camera dome consists of number ofcameras arranged in layers to monitor a hemisphere aroundits center. In volumetric surveillance,a 3D space is required tobemonitoredwhich can be achievedby implementing numberof multi-camera domes. A monitoring height is consideredas a constraint to ensure full coverage of the space belowit. Accordingly, the multi-camera dome can be redesignedinto a cylinder such that each of its multiple layers hasdifferent coverage radius. Minimum monitoring constraintsshould be met at all layers. This work is presenting a costoptimized design for the multi-camera dome that maximizesits coverage. The cost per node and number of squaremetersper dollar of multiple configurations are calculated using asearch space of cameras and considering a set of monitoring and coverage constraints. The proposed design is costoptimized per node and provides more coverage as compared to the hemispherical multi-camera dome.
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| 4. |
- Carratu, M., et al.
(författare)
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A CNN-based approach to measure wood quality in timber bundle images
- 2021
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Ingår i: 2021 IEEE International Instrumentation and Measurement Technology Conference (I2MTC). - : IEEE. - 9781728195391
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Konferensbidrag (refereegranskat)abstract
- At present, the Smart Industry is becoming a field of great interest for many worldwide researchers since it allows to experiment and research new advanced techniques. One of the most common explored approaches in operations where image processing has already been a milestone is the use of Convolutional Neural Networks (CNN). Those networks have enhanced the current image processing algorithms, achieving an improvement in decision processes usually based on human experience, where an analytical model is not always available. This paper proposes a novel approach for measuring the number of rotted logs in timber bundles using a CNN trained on thousands of timber log images extracted from bundles. Today, the Swedish forest industry bases the selling price of timber bundles on the evaluation of a visual inspection. This operation is based on human experience to evaluate and measure timber bundles' features, which is necessary to categorize them. The proposed approach has shown promising results compared to the actual visual inspection made by operators showing an F1 score with the best CNN architecture of 0.89.
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| 5. |
- Carratú, Marco, et al.
(författare)
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A novel IVS procedure for handling Big Data with Artificial Neural Networks
- 2020
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Ingår i: 2020 IEEE International Instrumentation and Measurement Technology Conference (I2MTC). - : IEEE. - 9781728144603
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Konferensbidrag (refereegranskat)abstract
- In recent times, thanks to the availability of a large quantity of data coming from the industrial process, several techniques based on a data-driven approach could be developed. Between all the data-driven techniques, as Principle Component Regression, Support Vector Machines, Artificial Neural Networks, Neuro-Fuzzy Systems, and many others, the data on which they rely should be analyzed to find correlations and dependencies that could improve their design. For this reason, the Input variable Selection (IVS) process has become of great interest in the recent period. The classical IVS relies on classical statistics, as Pearson coefficients, able to discover linear dependencies among data; today, due to the significant amount of data available, the challenge of also discovering non-linear dependencies appears to be a necessary skill, mainly for the design and development of a neural network. This paper proposes the use of a novel statistical tool named Maximal Information Coefficient (MIC) for developing an IVS procedure able to discover dependencies in a considerable dataset and guide the IVS designer to the selection of input variables in a data-driven application. As a case study, the procedure will be applied to a real application developed in the context of the Swedish forest industry, in order to choose the input variables of a neural network able to estimate the timber bundles volume, which represents an expensive parameter to measure in this context.
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| 6. |
- Carratu, M., et al.
(författare)
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An innovative method for log diameter measurements based on deep learning
- 2023
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Ingår i: 2023 IEEE International Instrumentation and Measurement Technology Conference (I2MTC). - : IEEE. - 9781665453837
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Konferensbidrag (refereegranskat)abstract
- The widespread adoption of Deep Learning techniques for Computer Vision in recent years has brought major changes to the world of industry, contributing greatly to this sector's transition to Industry 4.0, also referred to as Smart Industry. This involves an increasingly predominant role of machines and automation within industrial processes. In this context, the Swedish forest industry is an excellent context for applying these techniques. In particular, this work will deal with automating the measurement of log diameters to date carried out manually by operators in the industry. The proposed methodology will use two object detection neural networks, one deputed to detect logs in the scene and the other for the calibrated target. The latter thus allows the camera calibration to be fully automated, enabling each diameter to be measured without any further operations by the operator. The results obtained are satisfactory and open the way for the industrial application of the proposed methodology.
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| 7. |
- Carratù, M., et al.
(författare)
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Vision-Based System for Measuring the Diameter of Wood Logs
- 2023
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Ingår i: IEEE Open Journal of Instrumentation and Measurement. - : IEEE. - 2768-7236. ; 2, s. 1-12
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Tidskriftsartikel (refereegranskat)abstract
- Detecting and measuring objects with vision-based systems in uncontrolled environments is a difficult task that today, thanks to the development of increasingly advanced artificial intelligence-based techniques, can be solved with greater ease. In this context, this article proposes a novel approach for the vision-based measurement of objects in uncontrolled environments using a specific type of convolutional neural network (CNN) named you only look once (YOLO) and a direct linear transformation (DLT) process. The case study concerned designing a novel vision-based system for measuring the diameter of wood logs cut and loaded onto trucks. This problem has been occurring in the Swedish forestry industry. In fact, this operation is not carried out with computer vision algorithms because of the high variability of environmental conditions caused by the changing position of the sun, weather conditions, and the variability of truck positioning. To solve this problem, the YOLO network is proposed to locate logs while attempting to maintain a high Intersection over Union (IoU) value for the correct estimation of log size. Furthermore, in order to obtain accurate measurements, the DLT is used to convert into world coordinates the dimensions of the logs themselves. The proposed CNN-based solution is described after briefly introducing today’s methodologies adopted for wood bundle analysis. Particular attention is paid to both the training and the calibration steps. Results report that for 80% of cases, the error reported has been smaller than 4 cm, representing only 8% of the measurement, considering a mean log diameter for the application of 50 cm.
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| 8. |
- Carratù, Marco, et al.
(författare)
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Wireless Sensor Network Calibration for PM10 Measurement
- 2020
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Ingår i: 2020 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA). - : IEEE. - 9781728144337
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Konferensbidrag (refereegranskat)abstract
- The proposal of an Advanced Metering Infrastructure based on short-range communication is suggested for the continuous monitoring of Particulate Matter. A prototype of Automatic Measurement System (AMS), including a low-cost off-the-shelf PM sensor, has been developed as a remote node to be adopted in the radio Local Area Network. The results of the system calibration and comparison with the data quality requirements of the PM measurement according to European regulations, as well as the simulation of a typical Smart City scenario in terms of communication performance, confirm the feasibility of the proposed distributed AMS for an effective adoption within an urban area.
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| 9. |
- Fedorov, Igor, et al.
(författare)
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A two-layer 3D reconstruction method and calibration for multi-camera-based volumetric positioning and characterization
- 2021
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Ingår i: IEEE Transactions on Instrumentation and Measurement. - 0018-9456 .- 1557-9662. ; 70
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Tidskriftsartikel (refereegranskat)abstract
- A three-dimensional (3D) reconstruction method and multi-camera calibration using multiple artificial reference markers have been used for precise volumetric surveillance of fast-flying objects. The method uses a two-layer 3D reconstruction that integrates two multi-camera stereo-nodes. The fields of view of stereo nodes are directed at an acute angles to each other to provide greater coverage with the given constraints and to determine the flight characteristics of objects in 3D. The object’s flight reconstruction includes a “rough” estimation of its positions relative to selected artificial reference points in both stereo nodes separately and subsequent “refinement” of calculated positions. In this paper, we describe the proposed method and calibration technique, using a multi-camera system to measure object characteristics in 3D. The proposed method applies to volumetric surveillance in situations where it is necessary to count, track, and analyze the activities of flying objects, especially birds, using high spatial resolution.
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| 10. |
- Forsström, Stefan, 1984-, et al.
(författare)
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Specialanpassade kurser för yrkesverksamma ingenjörer : Erfarenheter och upplevelser
- 2023
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Ingår i: Bidrag från den 9:e utvecklingskonferensen för Sveriges ingenjörsutbildningar. - : Mälardalens universitet. - 9789174856200 ; , s. 348-353
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- I dagens samhälle blir det allt viktigare att fortbilda sig under hela sitt yrkesverksamma liv. För att möta efterfrågan på det livslånga lärandet har Mittuniversitetet utvecklat och genomfört ett antal kurser som riktar sig mot yrkesverksamma ingenjörer. Detta arbete presenterar våra erfarenheter av att ge dessa kurser, med en tyngdpunkt på studenternas upplevelser. Syftet med detta är att bygga upp en vetenskaplig bas för vad vi gör som är bra, men även vad som kan förbättras och förändras. Målsättningen är att göra dessa specialanpassade kurser riktade mot yrkesverksamma ingenjörer så givande och flexibla som möjligt. Våra initiala resultat visar bland annat att studenternas negativa upplevelser ofta var kopplade till antagningsförfarandet och det praktiska genomförandet av kurserna. Man hade svårigheter med att hitta hur man skulle registrera sig på kursen och att tidsramen för registrering kunde vara ett problem. Läroplattformen uppfattades som svår att överblicka och det förekom även viss otydlighet gällande var undervisningen skulle äga rum. Den positiva responsen i utvärderingarna gällde främst det faktiska kursinnehållet, då man ansåg att uppgifter och kursmaterial var givande. Vidare uppskattades kursupplägget, att man kunde kombinera studierna med arbete. Framledes kommer vi att fortsätta med dessa utvärderingar i takt med att kurserna ges, och därefter anpassa vårt mottagande och kommunikationen med studenterna. Även kursupplägget ses över kontinuerligt via den återkoppling vi mottar.
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