优化YOLOv8检测停车位，对比多个自定义主干网络性能
Optimizing YOLOv8 for Parking Space Detection: Comparative Analysis of Custom YOLOv8 Architecture
- 采用ResNet-18、VGG16等主干网络改进YOLOv8
- 在PKLot数据集上验证，不同结构精度与效率差异明显
- 适合智能停车系统中需平衡速度与准确性的场景
停车位占用检测是智能停车管理系统的关键环节。传统目标检测方法如YOLOv8虽能在停车场实现快速精准的车辆检测，但在部分可见车辆、小型车辆（如摩托车）及光线不足等边界情况下表现不佳。本文针对集成于YOLOv8的定制化主干网络进行综合对比分析，具体评估了ResNet-18、VGG16、EfficientNetV2和Ghost等多种主干网络在PKLot数据集上的检测精度与计算效率。实验结果揭示了各架构的优势与权衡，为停车占用检测任务中模型选型提供了依据。
原文摘要 · Abstract (English)
Parking space occupancy detection is a critical component in the development of intelligent parking management systems. Traditional object detection approaches, such as YOLOv8, provide fast and accurate vehicle detection across parking lots but can struggle with borderline cases, such as partially visible vehicles, small vehicles (e.g., motorcycles), and poor lighting conditions. In this work, we perform a comprehensive comparative analysis of customized backbone architectures integrated with YOLOv8. Specifically, we evaluate various backbones -- ResNet-18, VGG16, EfficientNetV2, Ghost -- on the PKLot dataset in terms of detection accuracy and computational efficiency. Experimental results highlight each architecture's strengths and trade-offs, providing insight into selecting suitable models for parking occupancy.
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