Xianglong Zhou

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About

Xianglong Zhou is a leading researcher in computer vision and deep learning, with a primary focus on lightweight object detection for challenging underwater environments. His work addresses the critical need for efficient, real-time visual perception in automated fishing and marine robotics. Zhou’s major contribution is the development of YOLOv6-ESG, a novel, streamlined detection framework that significantly improves accuracy and speed for identifying seafood in complex underwater imagery. This method, detailed in his highly cited 2023 paper (19 citations), tackles the inherent difficulties of low visibility, color distortion, and variable lighting in aquatic scenes. By optimizing convolutional neural network architectures for deployment on resource-constrained underwater robots, Zhou’s research bridges the gap between cutting-edge AI and practical marine applications. His work is pivotal for advancing sustainable aquaculture and autonomous underwater operations, demonstrating how tailored deep learning solutions can transform real-world industrial and environmental monitoring tasks.