Chenkai Guo

Jiangsu University of Science and Technology

Papers

Total Citations

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About

Chenkai Guo is a researcher advancing the field of underwater robotics and computer vision, with a primary focus on improving autonomous detection systems in challenging marine environments. His most-cited work, "Underwater Robot Target Detection Algorithm Based on YOLOv8" (2024), has already garnered 13 citations, addressing critical limitations in underwater identification caused by poor visibility, light attenuation, and complex backgrounds. Guo’s major contribution lies in enhancing the YOLOv8 architecture to boost detection accuracy and speed for underwater robots, enabling more reliable real-time object recognition in deep-sea exploration and monitoring tasks. This work is pivotal for applications in marine biology, environmental surveillance, and offshore infrastructure inspection. By tackling the persistent challenges of low contrast and distorted imagery, Guo’s research directly supports the development of smarter, more autonomous underwater vehicles. His innovative approach not only improves algorithmic performance but also bridges the gap between theoretical computer vision and practical oceanic deployment, making his contributions highly relevant for students and researchers working at the intersection of robotics, AI, and marine science.