Comparative Analysis of Custom YOLOv8 Backbones

layout: page title: Comparative Analysis of Custom YOLOv8 Backbones description: Optimizing YOLOv8 backbones for parking space detection img: assets/img/12.jpg importance: 1 category: work related_publications: true —

This project focuses on optimizing YOLOv8 backbone architectures for parking space detection using the PKLot dataset. We explored multiple lightweight and high-performance backbone models to achieve better precision–recall trade-offs, reduced inference latency, and enhanced computational efficiency for real-time applications.

Project Page

You can find the project pagehere.

Highlights

• Custom Backbone Architectures: ResNet-18, VGG16, EfficientNet-B0, and GhostNet-P2 modifications.
• Performance Metrics: Achieved a mAP@0.5:0.95 of 98.6%, outperforming baseline YOLOv8.
• Deployment Ready: Optimized for real-time edge devices with reduced model size and faster inference.
• Dataset: PKLot dataset of parking lot images with high variability in lighting, angle, and occlusion.

Key Contributions

• Designed and implemented custom lightweight backbones tailored for constrained hardware.
• Conducted extensive ablation studies analyzing accuracy, latency, and resource usage.
• Developed a streamlined training pipeline for rapid prototyping and benchmarking.