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

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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% mAP@0.5:0.95, 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.