NICASU: Neurotransmitter Inspired Cognitive AI Architecture for Surveillance Underwater

Abstract

The human brain is exceedingly good at learning rich narratives from highly limited experiences. One of the ways this is achieved in our brain is through neuromodulators or neurotransmitters, such as dopamine and nor-epinephrine, in cortical circuits. In terms of symbolic processing, these neuromodulators add “salience” to various emotions and experiences. A salience-based neural network (SANN) architecture was proposed in [1]. We have taken this architecture and have developed a discriminator to enable efficient change detection for underwater applications. 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. Several researchers working on the same tried to develop different techniques for identifying moving objects from outdoor scenes. However, while applying the same for underwater environments, it is found to be unable to preserve the minute details that are important for defining an object's boundary. This is mainly due to the complex scene dynamics of the aquatic environment. Moreover, the intricate natural properties of water and some of its characteristics, such as excessive turbidity, scattering, and low visibility, also make the task of detecting the object present in underwater videos extremely challenging. In this regard, we put forth an adversarial learning-based end-to-end deep learning architecture inspired by the way neurotransmitters work in the human brain to detect underwater moving objects. The proposed architecture uses two modules for underwater object detection. The initial module is a generator composed of a probabilistic learner which is based on multiple down- and up-sampling modules. Further, the discriminator network is composed of a multilevel feature-concatenation component, which can perpetuate specifics at distinct levels. The effectiveness of the proposed method (PM) is confirmed using the underwater change detection and Fish4Knowledge benchmark datasets by contrasting its outcomes with those of different state-of-the-art methods.