Machine learning-driven pedestrian detection and classification for electric vehicles: integrating Bayesian component network analysis and reinforcement region-based convolutional neural networks

Autonomous electric vehicle safety is crucially dependent on the accurate recognition of pedestrians in diverse situations. Current pedestrian detection techniques, however, face significant limitations due to reduced visibility and poor-quality images under low-lighting scenarios. With the aim of overcoming these challenges, this article proposes a novel, sustainable method for pedestrian detection and classification in electric vehicles using machine learning techniques. The approach processes video frame-based images as input, removing noise and smoothing the images for improved detection. A Bayesian component network analysis is employed to refine the features of the filtering-based boundary box detection, further enhancing the detection process. The selected features are then classified using a fully connected kernel operation based on the region with reward Q-Reinforcement architecture, resulting in a secure and efficient pedestrian detection system. The proposed method was evaluated on multiple image datasets using average precision, an area under the curve (AUC), log-average miss rate (MR), and root-mean-square error (RMSE) as performance measures. The experimental results demonstrated an average precision of 92%, MR of 48%, AUC of 56%, and RMSE of 61%. These findings indicate that the proposed technique effectively enhances pedestrian detection and classification for autonomous electric vehicles, contributing to increased safety and reliability in real-world applications.