YOLO-APD: Enhancing YOLOv8 for Robust Pedestrian Detection on Complex Road Geometries
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Date
2025-06
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International Journal of Computer Trends and Technology
Abstract
- Autonomous vehicle perception systems require robust pedestrian detection, particularly on geometrically complex
roadways like Type-S curved surfaces, where standard RGB camera-based methods face limitations. This paper introduces
YOLO-APD, a novel deep learning architecture enhancing the YOLOv8 framework specifically for this challenge. YOLO-APD
integrates several key architectural modifications: a parameter-free SimAM attention mechanism, computationally efficient
C3Ghost modules, a novel SimSPPF module for enhanced multi-scale feature pooling, the Mish activation function for improved
optimization, and an Intelligent Gather & Distribute (IGD) module for superior feature fusion in the network's neck. The concept
of leveraging vehicle steering dynamics for adaptive region-of-interest processing is also presented. Comprehensive evaluations
on a custom CARLA dataset simulating complex scenarios demonstrate that YOLO-APD achieves state-of-the-art detection
accuracy, reaching 77.7% mAP@0.5:0.95 and exceptional pedestrian recall exceeding 96%, significantly outperforming
baseline models, including YOLOv8.
Furthermore, it maintains real-time processing capabilities at 100 FPS, showcasing a superior balance between accuracy
and efficiency. Ablation studies validate the synergistic contribution of each integrated component. Evaluation on the KITTI
dataset confirms the architecture's potential while highlighting the need for domain adaptation. This research advances the
development of highly accurate, efficient, and adaptable perception systems based on cost-effective sensors, contributing to
enhanced safety and reliability for autonomous navigation in challenging, less-structured driving environments.
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Joctum, A., & Kandiri, J. (2025). YOLO-APD: Enhancing YOLOv8 for Robust Pedestrian Detection on Complex Road Geometries. arXiv preprint arXiv:2507.05376.