Optimization-Oriented Hybrid Visual Perception Architecture for Safety-Aware Pedestrian and Animal Detection in Autonomous Urban Mobility

Reliable visual perception is essential for autonomous urban mobility, where dense traffic, severe occlusion, low illumination, and heterogeneous object distributions challenge pedestrian and animal detection. This study proposes an optimization-oriented hybrid visual perception architecture that formulates visual perception as a unified optimization problem by jointly improving object localization, adaptive suppression, confidence calibration, feature refinement, and safety-oriented risk assessment. The proposed architecture integrates YOLOv11 for real-time object detection, Dynamic Non-Maximum Suppression (DNMS) for adaptive overlap filtering, EfficientNetB7 for hierarchical feature refinement, Bayesian confidence fusion for probabilistic confidence recalibration, and a mathematical risk assessment model for real-time decision support. The architecture was evaluated on COCO2017, Open Images Dataset V7, BDD100K, and a harmonized hybrid dataset and compared with representative CNN-based and YOLO-based baselines, including YOLOv5, YOLOv8, MobileNet, ResNet50, DenseNet121, and EfficientNetB7. The experimental results demonstrate that the proposed architecture consistently achieves superior performance, reaching 96% accuracy, 96% F1-score, 81% [email protected]:0.95, a 4% Miss Rate, a 3% False Positive Rate, and real-time inference at 25 Frames Per Second (FPS). Furthermore, the mathematical risk assessment model classifies detected objects into Safe, Warning, and Danger categories, enabling interpretable safety-aware decision support. The findings indicate that the proposed architecture offers an effective optimization-based solution for reliable and real-time perception in autonomous urban mobility.