An Enhanced Real-Time Object Detection Method using Liquid Neural Network and Echo State Network Architecture

Recent developments in Artificial Intelligence (AI) have significantly enhanced autonomous cars' object recognition capabilities, especially with the implementation of deep neural networks. Despite these advancements, achieving a harmonious balance between high precision and speed in vehicle contexts remains a considerable challenge. In this research, a novel object detection model is proposed to locate and identify objects in real-time videos using Liquid Neural Networks (LNNs) variant and Echo State Network (ESN) for model testing and training. One unique feature of LNNs is its capability to dynamically adjust their underlying mechanisms in response to constant streams of fresh data inputs, contributing to their adaptability in dynamic environments. In the analysis, the methodology of the process is presented, comparing LNNs with Artificial Neural Networks (ANNs) and highlighting the superiority of LNNs in the context of object identification for autonomous cars.