Image Processing Algorithms Running in Real-time on an Embedded System - Mon, Jul 1, 2019
My bachelor thesis research project.
[DESCRIPTION]
This document aims to provide an analysis of the current OpenCV Descriptors and Detectors performance when running in a real-time context. Understanding the efficiency and effectiveness of these tools is essential for developers and researchers working on computer vision applications. The performance metrics assessed are speed, accuracy, and reliability in various scenarios. By assessing their capabilities, we can identify areas for improvement and potential optimization strategies. Ultimately, this analysis will serve as a valuable resource for those looking to enhance their implementations using OpenCV.
In this paper, I presented several propositions aimed at showcasing OpenCV’s powerful computer vision capabilities for practical applications that can enhance the quality of life for persons with impaired vision. One of the key ideas I proposed involved the development of a specialized device designed to assist visually impaired users to navigate their surroundings by converting visual information into sound cues. This device would make use of advanced image processing techniques to detect environmental features, such as staircases and other obstacles commonly encountered in daily life, thus enabling users to avoid potential collisions and navigate in safer manner. Furthermore, I suggested an exciting application of these computer vision algorithms in the agricultural sector, where they could be integrated directly into the embedded hardware of drones. This approach would allow for a more efficient and targeted method of pesticide application, ensuring that only the crops requiring treatment would receive the necessary intervention, ultimately promoting sustainability and reducing unnecessary chemical usage while also enhancing crop health.