YOLOv8m Pothole Segmentation Model
| Property | Value |
|---|---|
| Library | Ultralytics v8.0.21 |
| Task Type | Image Segmentation |
| mAP@0.5 (mask) | 89.5% |
| mAP@0.5 (box) | 85.79% |
| Downloads | 4,715 |
What is yolov8m-pothole-segmentation?
This is a specialized computer vision model designed for detecting and segmenting potholes in road imagery. Built on the YOLOv8 architecture, it provides highly accurate instance segmentation of potholes, making it valuable for automated road inspection and maintenance systems.
Implementation Details
The model is implemented using the Ultralytics framework and requires ultralyticsplus version 0.0.23 and ultralytics 8.0.21. It features customizable inference parameters including confidence thresholds, IoU thresholds, and maximum detection limits.
- Confidence threshold: 0.25
- IoU threshold: 0.45
- Maximum detections per image: 1000
- Supports single-class detection: 'pothole'
Core Capabilities
- Instance segmentation of potholes with 89.5% mAP@0.5 for masks
- Bounding box detection with 85.79% mAP@0.5
- Real-time processing capability
- Support for both image and video inputs
Frequently Asked Questions
Q: What makes this model unique?
This model combines YOLOv8's speed with highly accurate pothole segmentation, achieving an impressive 89.5% mask mAP@0.5. It's specifically optimized for road infrastructure monitoring, making it particularly valuable for automated maintenance systems.
Q: What are the recommended use cases?
The model is ideal for automated road inspection systems, municipal maintenance departments, and infrastructure monitoring applications. It can be integrated into both real-time mobile applications and batch processing systems for road condition assessment.
