Global ETD Search

11	Detection of Pests in Agriculture Using Machine Learning Olsson, Emma January 2022 (has links) Pest inventory of a field is a way of knowing when the thresholds for pest controlis reached. It is of increasing interest to use machine learning to automate thisprocess, however, many challenges arise with detection of small insects both intraps and on plants.This thesis investigates the prospects of developing an automatic warning system for notifying a user of when certain pests are detected in a trap. For this, sliding window with histogram of oriented gradients based support vector machinewere implemented. Trap detection with neural network models and a check sizefunction were tested for narrowing the detections down to pests of a certain size.The results indicates that with further refinement and more training images thisapproach might hold potential for fungus gnat and rape beetles.Further, this thesis also investigates detection performance of Mask R-CNNand YOLOv5 on different insects in fields for the purpose of automating thedata gathering process. The models showed promise for detection of rape beetles. YOLOv5 also showed promise as a multi-class detector of different insects,where sizes ranged from small rape beetles to larger bumblebees. machine learning svm sliding window mask r-cnn yolov5 Annan elektroteknik och elektronik
12	Determination of Biomass in Shrimp-Farm using Computer Vision Tammineni, Gowtham Chowdary 30 October 2023 (has links) The automation in the aquaculture is proving to be more and more effective these days. The economic drain on the aquaculture farmers due to the high mortality of the shrimps can be reduced by ensuring the welfare of the animals. The health of shrimps can decline with even barest of changes in the conditions in the farm. This is the result of increase in stress. As shrimps are quite sensitive to the changes, even small changes can increase the stress in the animals which results in the decline of health. This severely dampens the mortality rate in the animals. Also, human interference while feeding the shrimps severely induces the stress on the shrimps and thereby affecting the shrimp’s mortality. So, to ensure the optimum efficiency of the farm, the feeding of the shrimps is made automated. The underfeeding and overfeeding also affects the growth of shrimps. To determine the right amount of food to provide for shrimps, Biomass is a very helpful parameter. The use of artificial intelligence (AI) to calculate the farm's biomass is the project's primary area of interest. This model uses the cameras mounted on top of the tank at densely populated areas. These cameras monitor the farm, and our model detects the biomass. By doing so, it is possible to estimate how much food should be distributed at that particular area. Biomass of the shrimps can be calculated with the help of the number of shrimps and the average lengths of the shrimps detected. With the reduced human interference in calculating the biomass, the health of the animals improves and thereby making the process sustainable and economical. info:eu-repo/classification/ddc/000 ddc:000 Objekterkennung; Aquakultur; Biomasse
13	Weed Detection in UAV Images of Cereal Crops with Instance Segmentation Gromova, Arina January 2021 (has links) Modern weeding is predominantly carried out by spraying whole fields with toxic pesticides, a process that accomplishes the main goal of eliminating weeds, but at a cost of the local environment. Weed management systems based on AI solutions enable more targeted actions, such as site-specific spraying, which is essential in reducing the need for chemicals. To introduce sustainable weeding in Swedish farmlands, we propose implementing a state-of-the-art Deep Learning (DL) algorithm capable of instance segmentation for remote sensing of weeds, before coupling an automated sprayer vehicle. Cereals have been chosen as the target crop in this study as they are among the most commonly cultivated plants in Northern Europe. We used Unmanned Aerial Vehicles (UAV) to capture images from several fields and trained a Mask R-CNN computer vision framework to accurately recognize and localize unique instances of weeds among plants. Moreover, we evaluated three different backbones (ResNet-50, ResNet101, ResNeXt-101) pre-trained on the MS COCO dataset and through transfer learning tuned the model towards our classification task. Some well-reported limitations in building an accurate model include occlusion among instances as well as the high similarity between weeds and crops. Our system handles these challenges fairly well. We achieved a precision of 0.82, recall of 0.61, and F1 score of 0.70. Still, improvements can be made in data preparation and pre-processing to further improve the recall rate. All and all, the main outcome of this study is the system pipeline which, together with post-processing using geographical field coordinates, could serve as a detector for half of the weeds in an end-to-end weed removal system. / Site-specific Weed Control in Swedish Agriculture computer vision deep learning CNN mask R-CNN weed detection Agricultural Science Jordbruksvetenskap
14	Hluboké neuronové sítě pro detekci anomálií při kontrole kvality / Deep Neural Networks for Defect Detection Juřica, Tomáš January 2019 (has links) The goal of this work is to bring automatic defect detection to the manufacturing process of plastic cards. A card is considered defective when it is contaminated with a dust particle or a hair. The main challenges I am facing to accomplish this task are a very few training data samples (214 images), small area of target defects in context of an entire card (average defect area is 0.0068 \% of the card) and also very complex background the detection task is performed on. In order to accomplish the task, I decided to use Mask R-CNN detection algorithm combined with augmentation techniques such as synthetic dataset generation. I trained the model on the synthetic dataset consisting of 20 000 images. This way I was able to create a model performing 0.83 AP at 0.1 IoU on the original data test set.
15	Learning to Measure Invisible Fish Gustafsson, Stina January 2022 (has links) In recent years, the EU has observed a decrease in the stocks of certain fish species due to unrestricted fishing. To combat the problem, many fisheries are investigating how to automatically estimate the catch size and composition using sensors onboard the vessels. Yet, measuring the size of fish in marine imagery is a difficult task. The images generally suffer from complex conditions caused by cluttered fish, motion blur and dirty sensors. In this thesis, we propose a novel method for automatic measurement of fish size that can enable measuring both visible and occluded fish. We use a Mask R-CNN to segment the visible regions of the fish, and then fill in the shape of the occluded fish using a U-Net. We train the U-Net to perform shape completion in a semi-supervised manner, by simulating occlusions on an open-source fish dataset. Different to previous shape completion work, we teach the U-Net when to fill in the shape and not by including a small portion of fully visible fish in the input training data. Our results show that our proposed method succeeds to fill in the shape of the synthetically occluded fish as well as of some of the cluttered fish in real marine imagery. We achieve an mIoU score of 93.9 % on 1 000 synthetic test images and present qualitative results on real images captured onboard a fishing vessel. The qualitative results show that the U-Net can fill in the shapes of lightly occluded fish, but struggles when the tail fin is hidden and only parts of the fish body is visible. This task is difficult even for a human, and the performance could perhaps be increased by including the fish appearance in the shape completion task. The simulation-to-reality gap could perhaps also be reduced by finetuning the U-Net on some real occlusions, which could increase the performance on the heavy occlusions in the real marine imagery. Instance Segmentation Shape Completion Automatic Size Measurement Fisheries Electronic Monitoring Mask R-CNN U-Net
16	Thermal Imaging-Based Instance Segmentation for Automated Health Monitoring of Steel Ladle Refractory Lining / Infraröd-baserad Instanssegmentering för Automatiserad Övervakning av Eldfast Murbruk i Stålskänk Bråkenhielm, Emil, Drinas, Kastrati January 2022 (has links) Equipment and machines can be exposed to very high temperatures in the steel mill industry. One particularly critical part is the ladles used to hold and pour molten iron into mouldings. A refractory lining is used as an insulation layer between the outer steel shell and the molten iron to protect the ladle from the hot iron. Over time, or if the lining is not completely cured, the lining wears out or can potentially fail. Such a scenario can lead to a breakout of molten iron, which can cause damage to equipment and, in the worst case, workers. Previous work analyses how critical areas can be identified in a proactive matter. Using thermal imaging, the failing spots on the lining could show as high-temperature areas on the outside steel shell. The idea is that the outside temperature corresponds to the thickness of the insulating lining. The detection of these spots is identified when temperatures over a given threshold are registered within the thermal camera's field of view. The images must then be manually analyzed over time, to follow the progression of a detected spot. The existing solution is also prone to the background noise of other hot objects. This thesis proposes an initial step to automate monitoring the health of refractory lining in steel ladles. The report will investigate the usage of Instance Segmentation to isolate the ladle from its background. Thus, reducing false alarms and background noise in an autonomous monitoring setup. The model training is based on Mask R-CNN on our own thermal images, with pre-trained weights from visual images. Detection is done on two classes: open or closed ladle. The model proved reasonably successful on a small dataset of 1000 thermal images. Different models were trained with and without augmentation, pre-trained weights as well multi-phase fine-tuning. The highest mAP of 87.5\% was achieved on a pre-trained model with image augmentation without fine-tuning. Though it was not tested in production, temperature readings could lastly be extracted on the segmented ladle, decreasing the risk of false alarms from background noise. Computer Vision Deep Learning Thermal Imaging Instance Segmentation Mask R-CNN Steel Ladle Breakout Prevention
17	Instance segmentation using 2.5D data Öhrling, Jonathan January 2023 (has links) Multi-modality fusion is an area of research that has shown promising results in the domain of 2D and 3D object detection. However, multi-modality fusion methods have largely not been utilized in the domain of instance segmentation. This master’s thesis investigated if multi-modality fusion methods can be applied to deep learning instance segmentation models to improve their performance on multi-modality data. The two multi-modality fusion methods presented, input extension and feature fusions, were applied to a two-stage instance segmentation model, Mask R-CNN, and a single-stage instance segmentation model, RTMDet. Models were trained on different variations of preprocessed RGBD and ToF data provided by SICK IVP, as well as RGBD data from the publicly available NYUDepth dataset. The master’s thesis concludes that the multi-modality fusion method presented as feature fusion can be applied to the Mask R-CNN model to improve the networks performance by 1.8%points (1.8%pt.) bounding box mAP and 1.6%pt. segmentation mAP on SICK RGBD, 7.7%pt. bounding box mAP and 7.4%pt. segmentation mAP on ToF, and 7.4%pt. bounding box mAP and 7.4%pt. segmentation mAP on NYUDepth. The RTMDet model saw little to no improvements from the inclusion of depth but had similar baseline performance as the improved Mask R-CNN model that utilized feature fusion. The input extension method saw no improvements to performance as it faced technical implementation limitations. instance segmentation multi-modality segmentation multi-modality fusion CNN RGBD ToF Mask R-CNN RTMDet MMDetection COCO NYUDepth Computer Engineering Datorteknik
18	3D Object Detection Using Virtual Environment Assisted Deep Network Training Dale, Ashley S. 12 1900 (has links) Indiana University-Purdue University Indianapolis (IUPUI) / An RGBZ synthetic dataset consisting of five object classes in a variety of virtual environments and orientations was combined with a small sample of real-world image data and used to train the Mask R-CNN (MR-CNN) architecture in a variety of configurations. When the MR-CNN architecture was initialized with MS COCO weights and the heads were trained with a mix of synthetic data and real world data, F1 scores improved in four of the five classes: The average maximum F1-score of all classes and all epochs for the networks trained with synthetic data is F1∗ = 0.91, compared to F1 = 0.89 for the networks trained exclusively with real data, and the standard deviation of the maximum mean F1-score for synthetically trained networks is σ∗ = 0.015, compared to σ_F1 = 0.020 for the networks trained exclusively with real F1 data. Various backgrounds in synthetic data were shown to have negligible impact on F1 scores, opening the door to abstract backgrounds and minimizing the need for intensive synthetic data fabrication. When the MR-CNN architecture was initialized with MS COCO weights and depth data was included in the training data, the net- work was shown to rely heavily on the initial convolutional input to feed features into the network, the image depth channel was shown to influence mask generation, and the image color channels were shown to influence object classification. A set of latent variables for a subset of the synthetic datatset was generated with a Variational Autoencoder then analyzed using Principle Component Analysis and Uniform Manifold Projection and Approximation (UMAP). The UMAP analysis showed no meaningful distinction between real-world and synthetic data, and a small bias towards clustering based on image background. Machine Learning MASK R-CNN ARTIFICIAL INTELLIGENCE IMAGE PROCESSING 3D IMAGE SIGNAL PROCESSING OBJECT DETECTION THREAT DETECTION VIRTUAL ENVIRONMENTS SYNTHETIC DATASET IMAGE SEGMENTATION RGBD RGBD VIDEO RGBZ ALGORITHM MS COCO TRANSFER LEARNING
19	Instance Segmentation of Multiclass Litter and Imbalanced Dataset Handling : A Deep Learning Model Comparison / Instanssegmentering av kategoriserat skräp samt hantering av obalanserat dataset Sievert, Rolf January 2021 (has links) Instance segmentation has a great potential for improving the current state of littering by autonomously detecting and segmenting different categories of litter. With this information, litter could, for example, be geotagged to aid litter pickers or to give precise locational information to unmanned vehicles for autonomous litter collection. Land-based litter instance segmentation is a relatively unexplored field, and this study aims to give a comparison of the instance segmentation models Mask R-CNN and DetectoRS using the multiclass litter dataset called Trash Annotations in Context (TACO) in conjunction with the Common Objects in Context precision and recall scores. TACO is an imbalanced dataset, and therefore imbalanced data-handling is addressed, exercising a second-order relation iterative stratified split, and additionally oversampling when training Mask R-CNN. Mask R-CNN without oversampling resulted in a segmentation of 0.127 mAP, and with oversampling 0.163 mAP. DetectoRS achieved 0.167 segmentation mAP, and improves the segmentation mAP of small objects most noticeably, with a factor of at least 2, which is important within the litter domain since small objects such as cigarettes are overrepresented. In contrast, oversampling with Mask R-CNN does not seem to improve the general precision of small and medium objects, but only improves the detection of large objects. It is concluded that DetectoRS improves results compared to Mask R-CNN, as well does oversampling. However, using a dataset that cannot have an all-class representation for train, validation, and test splits, together with an iterative stratification that does not guarantee all-class representations, makes it hard for future works to do exact comparisons to this study. Results are therefore approximate considering using all categories since 12 categories are missing from the test set, where 4 of those were impossible to split into train, validation, and test set. Further image collection and annotation to mitigate the imbalance would most noticeably improve results since results depend on class-averaged values. Doing oversampling with DetectoRS would also help improve results. There is also the option to combine the two datasets TACO and MJU-Waste to enforce training of more categories. Machine learning Multiclass Deep learning Instance segmentation Object segmentation Iterative stratification Mask R-CNN DetectoRS Imbalanced dataset Classification Detection Segmentation Litter Trash TACO COCO MMDetection Multinomial Cybercom AI Artificial intelligence Land-based litter Computer vision Maskininlärning Djupinlärning Instanssegmentering Objektsegmentering Mask R-CNN DetectoRS Obalanserat dataset Klassificering Detektion Segmentering Skräp TACO COCO MMDetection Multinomial Cybercom AI Artificiell intelligens Datorseende
20	Deep Learning with Vision-based Technologies for Structural Damage Detection and Health Monitoring Bai, Yongsheng 08 December 2022 (has links) No description available. Civil Engineering Computer Science Mechanics deep learning structural damage classification structural damage detection crack detection spalling detection ResNet U-Net cascaded networks Mask R-CNN structural health monitoring shaking table tests Lucas-Kanade tracker displacement subtraction frequency subtraction progressive collapse LiDAR camera drones.

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