Potholes Detection and Segmentation using YOLOv8 (Images & Videos)| Custom Dataset | Complete Guide

Summary

In this tutorial, viewers learn how to implement pothole detection and segmentation using YOLOv8 and RoboFlow. The video covers account setup on RoboFlow, creating a dataset from videos, annotating images, and training the model in Google Colab. It emphasizes the importance of using a public workspace for data export and details the annotation process for image segmentation. After training, the model's performance is evaluated, showcasing its ability to detect potholes effectively. The tutorial concludes with insights on validating the custom model and the overall training process.

Takeaways

• 😀 Create a Roboflow account to manage and export datasets effectively.
• 📁 Choose a public workspace plan in Roboflow to enable data import and export.
• 🎥 Use video input to create a custom dataset by extracting frames at a rate of one per second.
• 📊 Clone additional images from public datasets in Roboflow to enrich your training set.
• ✏️ Annotate images carefully, marking potholes with polygon shapes to ensure accuracy.
• 🚀 After annotation, you can generate the dataset with preprocessing options like Auto Orient and Resize.
• 💻 Use Google Colab to train your YOLO V8 model, taking advantage of GPU acceleration.
• 📚 Clone the relevant GitHub repository for YOLO V8 to access necessary scripts and files.
• 🔍 Evaluate the model's performance using metrics such as mean average precision (mAP) during training.
• 📈 Visualize the confusion matrix to assess how well the model detects potholes and identify areas for improvement.

Q & A

What is the primary focus of the video tutorial?

The tutorial focuses on implementing pothole detection and segmentation using the YOLOv8 model.

Why is it important to choose a public plan when creating a workspace on Roboflow?

Choosing a public plan allows users to import and export datasets, which is essential for transferring data to Google Colab for training.

How many images can be generated from a two-minute video at one frame per second?

Approximately 130 images can be generated from a two-minute video when extracting one frame per second.

What steps are involved in cloning additional images for the dataset?

Users can search for publicly available datasets on Roboflow, select images, and clone them to their own dataset to enhance the number of training samples.

What is the annotation process described in the tutorial?

The annotation process involves manually marking potholes in each image using polygon shapes, allowing the model to learn where the potholes are located.

What adjustments are made to the dataset before training the model?

Before training, basic preprocessing like auto-orienting and resizing the images is applied to ensure consistency and improve model performance.

Why is Google Colab recommended for training the model?

Google Colab is recommended because it provides a free environment with GPU support, which significantly speeds up the training process.

What metrics are monitored during the model training?

During training, metrics such as mean average precision (mAP) and intersection over union (IoU) are monitored to evaluate the model's performance.

What does the confusion matrix reveal about the model's performance?

The confusion matrix provides insights into how well the model detects potholes, showing the ratio of true positives to false negatives and indicating areas for improvement.

What was the mean average precision achieved by the model after training?

The model achieved a mean average precision of 0.532 with IoU of 50, indicating moderate detection capability.