Background: Autonomous navigation has become increasingly popular. This surge in popularity caused a lot of interest in sensor technologies, driving the cost of sensor technology down. This has resulted in increasing developments in deep learning for computer vision. There is, however, not a lot of available, adaptable research for directly performing data augmentation on point cloud data independent of the training process. This thesis focuses on the impact of point cloud augmentation techniques on 3D object detection quality. Objectives: The objectives of this thesis are to evaluate the efficiency of geometric data augmentation techniques for point cloud data. The identified techniques are then implemented on a 3D object detector, and the results obtained are then compared based on selected metrics. Methods: This thesis uses two literature reviews to find the appropriate point cloud techniques to implement for data augmentation and a 3D object detector to implement data augmentation. Subsequently, an experiment is performed to quantitatively discern how much improvement augmentation offers in the detection quality. Metrics used to compare the algorithms include precision, recall, average precision, mean average precision, memory usage and training time. Results: The literature review results indicate flipping, scaling, translation and rotation to be ideal candidates for performing geometric data augmentation and ComplexYOLO to be a capable detector for 3D object detection. Experimental results indicate that at the expense of some training time, the developed library "Aug3D" can boost the detection quality and results of the ComplexYOLO algorithm. Conclusions: After analysis of results, it was found that the implementation of geometric data augmentations (namely flipping, translation, scaling and rotation) yielded an increase of over 50% in the mean average precision for the performance of the ComplexYOLO 3D detection model on the Car and Pedestrian classes.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:bth-22329 |
| Date | January 2021 |
| Creators | Kapoor, Shrayash |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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