Exploring Machine Learning Engineering for Object Detection and Tracking by Unmanned Aerial Vehicle (UAV)

With the advancement of deep learning methods it is imperative that autonomous systems will increasingly become intelligent with the inclusion of advanced machine learning algorithms to execute a variety of autonomous operations. One such task involves the design and evaluation for a subsystem of the perception system for object detection and tracking. The challenge in the creation of software to solve the task is in discovering the need for a dataset, annotation of the dataset, selection of features, integration and refinement of existing algorithms, while evaluating performance metrics through training and testing. This research effort focuses on the development of a machine learning pipeline emphasizing the inclusion of assurance methods with increasing automation. In the process, a new dataset was created by collecting videos of moving object such as Roomba vacuum cleaner, emulating search and rescue (SAR) for indoor environment. Individual frames were extracted from the videos and labeled using a combination of manual and automated techniques. This annotated dataset was refined for accuracy by initially training it on YOLOv4. After the refinement of the dataset it was trained on a second YOLOv4 and a Mask R-CNN model, which is deployed on a Parrot Mambo drone to perform real-time object detection and tracking. Experimental results demonstrate the effectiveness of the models in accurately detecting and tracking the Roomba across multiple trials, achieving an average loss of 0.1942 and 96% accuracy.

AssistTaxi: A Comprehensive Dataset for Taxiway Analysis and Autonomous Operations

The availability of high-quality datasets play a crucial role in advancing research and development especially, for safety critical and autonomous systems. In this paper, we present AssistTaxi, a comprehensive novel dataset which is a collection of images for runway and taxiway analysis. The dataset comprises of more than 300,000 frames of diverse and carefully collected data, gathered from Melbourne (MLB) and Grant-Valkaria (X59) general aviation airports. The importance of AssistTaxi lies in its potential to advance autonomous operations, enabling researchers and developers to train and evaluate algorithms for efficient and safe taxiing. Researchers can utilize AssistTaxi to benchmark their algorithms, assess performance, and explore novel approaches for runway and taxiway analysis. Addition-ally, the dataset serves as a valuable resource for validating and enhancing existing algorithms, facilitating innovation in autonomous operations for aviation. We also propose an initial approach to label the dataset using a contour based detection and line extraction technique.

ALINA: Advanced Line Identification and Notation Algorithm

Labels are the cornerstone of supervised machine learning algorithms. Most visual recognition methods are fully supervised, using bounding boxes or pixel-wise segmentations for object localization. Traditional labeling methods, such as crowd-sourcing, are prohibitive due to cost, data privacy, amount of time, and potential errors on large datasets. To address these issues, we propose a novel annotation framework, Advanced Line Identification and Notation Algorithm (ALINA), which can be used for labeling taxiway datasets that consist of different camera perspectives and variable weather attributes (sunny and cloudy). Additionally, the CIRCular threshoLd pixEl Discovery And Traversal (CIRCLEDAT) algorithm has been proposed, which is an integral step in determining the pixels corresponding to taxiway line markings. Once the pixels are identified, ALINA generates corresponding pixel coordinate annotations on the frame. Using this approach, 60,249 frames from the taxiway dataset, AssistTaxi have been labeled. To evaluate the performance, a context-based edge map (CBEM) set was generated manually based on edge features and connectivity. The detection rate after testing the annotated labels with the CBEM set was recorded as 98.45%, attesting its dependability and effectiveness.