A Spiking Neural Network Approach for Autonomous Underwater Object Classification Through Bio-Inspired Deep Learning and Edge Computing - S. Jayanthi (International Journal of Intelligent Systems and Applications in Engineering, 2024)
Underwater Object Classification (UOC) is a critical task for Autonomous Underwater Vehicles (AUVs) engaged in underwater exploration and environmental monitoring. This paper explores the integration of bio-inspired deep learning techniques, particularly Spiking Neural Networks (SNN), with edge computing paradigms utilizing Field-Programmable Gate Arrays (FPGAs) and Graphics Processing Units (GPUs), coupled with the Constrained Application Protocol (CoAP) communication protocol. The application of Deep Q-Networks (DQN) for reinforcement learning-based object classification is investigated. The proposed framework aims to enhance the autonomy, efficiency, and adaptability of AUVs in discerning and classifying underwater objects in real-time scenarios. By leveraging the inherent parallelism and energy efficiency of SNNs, along with the computational capabilities of FPGAs and GPUs for accelerated inference, AUVs can perform object classification tasks onboard with reduced latency and energy consumption. Moreover, the integration of CoAP facilitates seamless communication between AUVs and remote servers for data exchange and collaborative decision-making. The utilization of DQN enables AUVs to learn and adapt their classification strategies based on feedback from the environment, thereby improving their performance over time. The proposed approach demonstrates promising results in underwater object classification through experimental validation and simulation studies, paving the way for advanced applications in underwater robotics and exploration. Through experimental validation, the system achieves a remarkable increase in classification accuracy by 15%, as evidenced by adaptability scores ranging from 7.5 to 8.9. These results signify a significant advancement in underwater robotics, paving the way for more efficient and precise exploration and monitoring of underwater environments.Top of Form