Underwater Change Detection Using Multiple Sampling-Based Probabilistic Learner and Feature Preservance Discriminator

Surveillance can be defined as the process of monitoring the behavior and activities of different objects to generate meaningful insights into a video scene. In the context of underwater, surveillance can be elucidated as one of the processes of detecting and tracking the moving objects present in underwater videos. Many methods have been put forth to separate moving objects from underwater environments. Nevertheless, such methods cannot maintain the minute details that are crucial for determining an object’s boundary. This is mainly due to the intricate natural properties of water and some of its characteristics, such as excessive turbidity, scattering, low visibility, etc. In this regard, we put forth an adversarial learning-based end-to-end deep learning architecture to detect underwater moving objects. The proposed architecture uses two modules for underwater object detection. The initial module is a generator comprised of a probabilistic learner which is based on multiple down-sampling and up-sampling modules. Further, the discriminator network is composed of a multi-level feature concatenation module which can perpetuate specifics at distinct levels. The effectiveness of the proposed method is confirmed using two underwater benchmark datasets by contrasting its outcomes with those of eight state-of-the-art methods.