| 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. |
- Lundström, Adam, et al.
(författare)
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An interactive threshold-setting procedure for improved multivariate anomaly detection in time series
- 2023
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Ingår i: IEEE Access. - : IEEE. - 2169-3536. ; 11, s. 93898-93907
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Tidskriftsartikel (refereegranskat)abstract
- Anomaly detection in multivariate time series is valuable for many applications. In this context, unsupervised and semi-supervised deep learning methods that estimate how normal a new observation is have shown promising results on benchmark datasets. These methods are dependent on a threshold that determines which points should be regarded as anomalous and not be anomalous. However, finding the optimal threshold is not easy since no information about the ground truth is known in advance, which implies that there are limitations to automatic threshold-setting methods available today. An alternative is to utilize the expertise of users that can interact in a threshold-setting procedure, but for this to be practically feasible, the method needs to be both accurate and efficient in relation to the state-of-the-art automatic methods. Therefore, this study develops an interactive threshold-setting schema and examines to what extent it can outperform the current state-of-the-art automatic threshold-setting methods. The result of the study strongly indicates that the suggested method with little effort can provide higher accuracy than the automatic threshold-setting methods on a general basis.
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| 4. |
- Lundström, Adam, et al.
(författare)
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Improving deep learning based anomaly detection on multivariate time series through separated anomaly scoring
- 2022
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Ingår i: IEEE Access. - 2169-3536. ; 10, s. 108194-108204
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Tidskriftsartikel (refereegranskat)abstract
- The importance of anomaly detection in multivariate time series has led to the development of several prominent deep learning solutions. As a part of the anomaly detection method, the scoring method has shown to be of significant importance when separating non-anomalous points from anomalous ones. At this time, most of the solutions utilize an aggregated score which means that relevant information created by the anomaly detection model might be lost. Therefore, this study has set out to examine to what extent anomaly detection in multivariate time series based on deep learning can be improved if all the residuals from each individual channel is considered in the anomaly score. To achieve this, an aggregated and separated scoring method has been applied with a simple denoising convulutional autoencoder (DCAE). In addition, the performance has been compared with other state-of-the-art methods. The result showed that the separated approach has the potential to generate a significantly higher performance than the aggregated one. At the same time, there were some indications suggesting that an aggregated scoring is better at generalizing when no labels to base the anomaly thresholds on, are available. Therefore, the result should serve as an encouragement to use a separated scoring approach together with a small sample of labeled anomalies to optimise the thresholds. Lastly, due to the impact of the anomaly score, the result suggests that future research within this field should consider applying the same anomaly scoring method when comparing the performance of deep learning algorithms.
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| 5. |
- Razavi-Shearer, Devin M., et al.
(författare)
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Adjusted estimate of the prevalence of hepatitis delta virus in 25 countries and territories
- 2024
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Ingår i: JOURNAL OF HEPATOLOGY. - 0168-8278 .- 1600-0641. ; 80:2, s. 232-242
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Tidskriftsartikel (refereegranskat)abstract
- Background & Aims: Hepatitis delta virus (HDV) is a satellite RNA virus that requires the hepatitis B virus (HBV) for assembly and propagation. Individuals infected with HDV progress to advanced liver disease faster than HBV-monoinfected individuals. Recent studies have estimated the global prevalence of anti-HDV antibodies among the HBV-infected population to be 5-15%. This study aimed to better understand HDV prevalence at the population level in 25 countries/territories. Methods: We conducted a literature review to determine the prevalence of anti-HDV and HDV RNA in hepatitis B surface antigen (HBsAg)-positive individuals in 25 countries/territories. Virtual meetings were held with experts from each setting to discuss the findings and collect unpublished data. Data were weighted for patient segments and regional heterogeneity to estimate the prevalence in the HBV-infected population. The findings were then combined with The Polaris Observatory HBV data to estimate the anti-HDV and HDV RNA prevalence in each country/territory at the population level. Results: After adjusting for geographical distribution, disease stage and special populations, the anti-HDV prevalence among the HBsAg+ population changed from the literature estimate in 19 countries. The highest anti-HDV prevalence was 60.1% in Mongolia. Once adjusted for the size of the HBsAg+ population and HDV RNA positivity rate, China had the highest absolute number of HDV RNA+ cases. Conclusions: We found substantially lower HDV prevalence than previously reported, as prior meta-analyses primarily focused on studies conducted in groups/regions that have a higher probability of HBV infection: tertiary care centers, specific risk groups or geographical regions. There is large uncertainty in HDV prevalence estimates. The implementation of reflex testing would improve estimates, while also allowing earlier linkage to care for HDV RNA+ individuals. The logistical and economic burden of reflex testing on the health system would be limited, as only HBsAg+ cases would be screened.
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| 6. |
- Vilar, Cristian, et al.
(författare)
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Evaluation of 2D-/3D-Feet-Detection Methods for Semi-Autonomous Powered Wheelchair Navigation
- 2021
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Ingår i: Journal of Imaging. - : MDPI AG. - 2313-433X. ; 7:12
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Tidskriftsartikel (refereegranskat)abstract
- Powered wheelchairs have enhanced the mobility and quality of life of people with special needs. The next step in the development of powered wheelchairs is to incorporate sensors and electronic systems for new control applications and capabilities to improve their usability and the safety of their operation, such as obstacle avoidance or autonomous driving. However, autonomous powered wheelchairs require safe navigation in different environments and scenarios, making their development complex. In our research, we propose, instead, to develop contactless control for powered wheelchairs where the position of the caregiver is used as a control reference. Hence, we used a depth camera to recognize the caregiver and measure at the same time their relative distance from the powered wheelchair. In this paper, we compared two different approaches for real-time object recognition using a 3DHOG hand-crafted object descriptor based on a 3D extension of the histogram of oriented gradients (HOG) and a convolutional neural network based on YOLOv4-Tiny. To evaluate both approaches, we constructed Miun-Feet—a custom dataset of images of labeled caregiver’s feet in different scenarios, with backgrounds, objects, and lighting conditions. The experimental results showed that the YOLOv4-Tiny approach outperformed 3DHOG in all the analyzed cases. In addition, the results showed that the recognition accuracy was not improved using the depth channel, enabling the use of a monocular RGB camera only instead of a depth camera and reducing the computational cost and heat dissipation limitations. Hence, the paper proposes an additional method to compute the caregiver’s distance and angle from the Powered Wheelchair (PW) using only the RGB data. This work shows that it is feasible to use the location of the caregiver’s feet as a control signal for the control of a powered wheelchair and that it is possible to use a monocular RGB camera to compute their relative positions.
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