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ContributorsMcLin, Katherine (Performer) / Campbell, Andrew (Pianist) (Performer) / ASU Library. Music Library (Publisher)
Created2006-11-02
ContributorsDunger, Robert (Performer) / Clavijo, Donna (Performer) / ASU Library. Music Library (Publisher)
Created2004-04-24
ContributorsTerwilliger, William (Performer) / Cooperstock, Andrew (Performer) / Opus Two (Performer) / ASU Library. Music Library (Publisher)
Created2008-10-25
ContributorsElias-Rodriguez, Ricardo (Performer) / Wang, Liang-Yu (Performer) / ASU Library. Music Library (Publisher)
Created2007-12-02
ContributorsWang, Xi (Performer) / Vallecillos, Rosemary (Performer) / Ochi, Naoko (Performer) / Wright, Matthew (Performer) / Yu, Jenwei (Performer) / Laskus, Agnieszka (Performer) / Chein, Chai-I (Performer) / Smothers, Chrystal (Performer) / Helvey, Emily (Performer) / ASU Library. Music Library (Publisher)
Created2005-12-02
ContributorsWatras, Matthew (Performer) / Qualls, Karla J. (Performer) / ASU Library. Music Library (Publisher)
Created1990-12-06
ContributorsBivona, Kathryn (Performer) / Chen, Chia-I (Performer) / ASU Library. Music Library (Publisher)
Created2008-11-23
ContributorsAn, Zhihuan (Performer) / Hsieh, Hsaio-Hsi (Performer) / ASU Library. Music Library (Publisher)
Created2023-11-30
Description
The thesis explores the avenues of machine learning principles in object detection using TensorFlow 2 Object Detection API Libraries for implementation. Integrating object detection capabilities into ESP-32 cameras can enhance functionality in the capstone dragster application and potential applications, such as autonomous robots. The research implements the TensorFlow 2 Object Detection API, a widely used framework for training and deploying object detection models. By leveraging the pre-trained models available in the API, the system can detect a wide range of objects with high accuracy and speed. Fine-tuning these models using a custom dataset allows us to enhance their performance in detecting specific objects of interest. Experiments to identify strengths and weaknesses of each model's implementation before and after training using similar images were evaluated The thesis also explores the potential limitations and challenges of deploying object detection on real-time ESP-32 cameras, such as limited computational resources, costs, and power constraints. The results obtained from the experiments demonstrate the feasibility and effectiveness of implementing object detection on ESP-32 cameras using the TensorFlow2 Object Detection API. The system achieves satisfactory accuracy and real-time processing capabilities, making it suitable for various practical applications. Overall, this thesis provides a foundation for further advancements and optimizations in the integration of object detection capabilities into small, low-power devices such as ESP-32 cameras and a crossroad to explore its applicability for other image-capturing and processing devices in industrial, automotive, and defense sectors of industry.
ContributorsMani, Vinesh (Author) / Tsakalis, Konstantinos (Thesis director) / Jayasuriya, Suren (Committee member) / Barrett, The Honors College (Contributor) / Electrical Engineering Program (Contributor)
Created2024-05
ContributorsQuon, Joyce (Performer) / Campbell, Andrew (Pianist) (Performer) / ASU Library. Music Library (Publisher)
Created1999-10-16