Brake light detection of vehicles using differential evolution based neural architecture search

Abstract

Recognition of brake light status in vehicles is crucial for anticipating speed changes and preventing rear-end collisions for autonomous driving systems. Existing literature presents two types of methods for brake light detection: hand-crafted feature-based methods and deep learning-based methods. However, hand-crafted methods often struggle to capture brake light characteristics accurately in real-life conditions. In contrast, deep learning-based systems can adapt to diverse brake light variations across different vehicle types and environments. Nevertheless, manually designing Deep Neural Network (DNN) models requires expertise and is prone to errors. To address this limitation, we propose a novel approach that leverages Neural Architecture Search (NAS) to automatically generate optimal DNN architectures for object detection tasks, specifically for brake light detection. In contrast to the existing NAS approaches that focus on classification models, our technique explores NAS for object detection tasks. We employ a modified Differential Evolution algorithm, incorporating evaluation correction-based selection for mutation and species protection-based selection to identify the optimal DNN backbone architecture with optimal training parameters. The proposed approach achieved mean accuracy of 89.73 %, and 88.90 % on four-wheeler datasets CaltechGraz and UC Merced Vehicle Rear Signal datasets, respectively, and it has achieved 97.97 % on the proposed two-wheeler NITW-MBS dataset. The proposed approach’s generalization capability and practical applicability are ascertained through cross-dataset evaluation and experiments on real-world traffic video.