YOLO-APD: Enhancing YOLOv8 for Robust Pedestrian Detection on Complex Road Geometries

TL;DR

This paper presents YOLO-APD, an improved YOLOv8-based model designed for robust pedestrian detection on complex road geometries, achieving high accuracy and real-time performance in challenging autonomous driving scenarios.

Contribution

Introduction of YOLO-APD with novel modules and adaptive region processing, significantly enhancing pedestrian detection accuracy and efficiency on complex road surfaces.

Findings

Achieves 77.7% mAP@0.5:0.95 on CARLA dataset

Over 96% pedestrian recall in complex scenarios

Runs at 100 FPS in real-time applications

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.