Zhenling Ma

Papers

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About

Dr. Zhenling Ma is a leading researcher in computer vision and deep learning, with a particular focus on lightweight object detection for underwater and marine applications. Their most cited work, "YOLOv6-ESG: A Lightweight Seafood Detection Method" (2023, 19 citations), addresses the critical challenge of deploying efficient convolutional neural networks on underwater robots for automated fishing. By enhancing the YOLOv6 architecture, Dr. Ma developed a method that balances detection accuracy with computational efficiency, enabling real-time seafood identification in complex underwater environments. This contribution is pivotal for advancing autonomous underwater operations, reducing the computational burden on robotic systems while maintaining robust performance. Dr. Ma’s research bridges the gap between theoretical deep learning models and practical, resource-constrained applications, making significant strides toward sustainable and automated marine resource management. Their work has been recognized as a key reference in the growing field of underwater object detection, inspiring further innovations in lightweight neural network design for environmental monitoring and aquaculture.