Machine Learning-Based Depth Estimation for Energy Optimization in Smart Camera Systems

This paper presents a hybrid approach for energy management in smart camera systems by combining Convolutional Neural Networks (CNNs) for depth estimation, Stochastic Gradient Descent (SGD) for training, and modified Particle Swarm Optimization (PSO) for minimizing energy consumption. The proposed model deploys CNNs to accurately predict depth maps, while SGD fine-tunes the network’s weights to enhance prediction accuracy. On the other hand, the modified PSO algorithm optimizes camera settings to achieve energy savings without sacrificing depth estimation performance. The modification method consists of crossover and mutation operators to boost the global search ability of the algorithm. Experimental results on benchmark datasets, including KITTI and NYU Depth V2, demonstrate that the proposed hybrid model could successfully make a high reduction in energy consumption, with minimal loss in depth accuracy. Comparisons with baseline models show significant improvements in both energy efficiency and depth estimation precision.