In the past decade, advances in marine object recognition have been dramatically boosted for monitoring of underwater ecosystems. Traditional statistical analysis and ocean model simulation heavily depend on the availability of visual features. However, Due to light attenuation and scattering problem, the underwater images captured by optical imaging system are heavily degraded. As a result, … full description “Deep learning based underwater image segmentation and object recognition (available)”
Supervisor: Li Zhang
Active: updated 5-MAR-2024 (OSAA035) no expiry
Deep Learning-based Environmental Sound Classification (available)
Automatic sound classification attracts increasing research attention owing to its vast applications, such as robot navigation, environmental sensing, musical instrument classification, medical diagnosis, and surveillance. Sound classification tasks involve the extraction of acoustic characteristics from the audio signals and the subsequent identification of different sound classes. In this project, we will explore diverse deep neural … full description “Deep Learning-based Environmental Sound Classification (available)”
Video Action Classification (available)
Automatic interpretation of human actions from realistic videos attracts increasing research attention owing to its growing demand in real-world deployments such as biometrics, intelligent robotics, and surveillance. In this project, we will explore a variety of deep neural networks for video action classification, owing to their great efficiency in spatial-temporal feature learning.