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1	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
2	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

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Instance segmentation using 2.5D data

Instance Segmentation of Multiclass Litter and Imbalanced Dataset Handling : A Deep Learning Model Comparison / Instanssegmentering av kategoriserat skräp samt hantering av obalanserat dataset