Leilei Jin

About

Leilei Jin is a leading researcher in underwater autonomous systems, with a primary focus on automatic target recognition (ATR) using deep learning. His most influential work, "Accurate Underwater ATR in Forward-Looking Sonar Imagery Using Deep Convolutional Neural Networks" (2019, 57 citations), addresses a critical challenge in marine robotics: the difficulty of recognizing objects in complex, low-visibility underwater environments. While traditional methods rely on hand-crafted features and classifiers that often fall short, Jin pioneered the application of deep convolutional neural networks to forward-looking sonar imagery, achieving a significant leap in recognition accuracy. This contribution has not only advanced the field of underwater computer vision but also has practical implications for autonomous navigation, mine detection, and marine exploration. Jin’s work bridges the gap between classical signal processing and modern deep learning, offering a robust solution for real-world marine operations. His research continues to influence the development of intelligent underwater vehicles, making him a key figure in the intersection of robotics, sonar technology, and artificial intelligence.

Research Focus

Key Achievements

Top Papers

1. 1
   Accurate Underwater ATR in Forward-Looking Sonar Imagery Using Deep Convolutional Neural Networks

   57 citations · 2019

Key Collaborators

Contact & Links