This review paper investigates the convergence of Unmanned Aerial Vehicle (UAV) technology, semantic segmentation algorithms, and real-time task scheduling on embedded RISC-V platforms. UAVs are increasingly utilized in diverse applications, necessitating efficient onboard processing for tasks such as object detection, environmental mapping, and autonomous navigation. Semantic segmentation, a crucial computer vision technique, enables pixel-level understanding of UAV-captured imagery. However, the computational demands of semantic segmentation algorithms pose a challenge for resource-constrained embedded systems. The RISC-V architecture, an open-source instruction set architecture (ISA), offers a promising solution for developing energy-efficient and customizable hardware platforms for UAVs. This paper provides a comprehensive overview of the current state-of-the-art in UAV semantic segmentation, real-time task scheduling methodologies, and the utilization of RISC-V platforms in this domain. We examine various semantic segmentation algorithms optimized for embedded deployment, focusing on their accuracy, computational complexity, and memory footprint. We also explore different real-time task scheduling techniques employed to manage the execution of semantic segmentation and other critical tasks on UAVs, considering factors such as latency, jitter, and resource utilization. Furthermore, we analyze the advantages and challenges of leveraging RISC-V processors for UAV applications, highlighting their potential for customization, energy efficiency, and security. Finally, we identify key research gaps and future directions in this rapidly evolving field, emphasizing the need for developing novel hardware-software co-design methodologies to enable robust and efficient UAV semantic segmentation on embedded RISC-V platforms. This review contributes to a deeper understanding of the opportunities and challenges in deploying advanced computer vision algorithms on UAVs, facilitating the development of intelligent and autonomous UAV systems.
