#yolov8

For anyone studying computer vision and semantic segmentation for environmental monitoring.

The primary technical challenge in implementing automated flood detection is often the disparity between available dataset formats and the specific requirements of modern architectures. While many public datasets provide ground truth as binary masks, models like YOLOv8 require precise polygonal coordinates for instance segmentation. This tutorial focuses on bridging that gap by using OpenCV to programmatically extract contours and normalize them into the YOLO format. The choice of the YOLOv8-Large segmentation model provides the necessary capacity to handle the complex, irregular boundaries characteristic of floodwaters in diverse terrains, ensuring a high level of spatial accuracy during the inference phase.

The workflow follows a structured pipeline designed for scalability. It begins with a preprocessing script that converts pixel-level binary masks into normalized polygon strings, effectively transforming static images into a training-ready dataset. Following a standard 80/20 data split, the model is trained with specific attention to the configuration of a single-class detection system. The final stage of the tutorial addresses post-processing, demonstrating how to extract individual predicted masks from the model output and aggregate them into a comprehensive final mask for visualization. This logic ensures that even if multiple water bodies are detected as separate instances, they are consolidated into a single representation of the flood zone.

Alternative reading on Medium: https://medium.com/@feitgemel/yolov8-segmentation-tutorial-for-real-flood-detection-963f0aaca0c3

Detailed written explanation and source code: https://eranfeit.net/yolov8-segmentation-tutorial-for-real-flood-detection/

Deep-dive video walkthrough: https://youtu.be/diZj_nPVLkE

This content is provided for educational purposes only. Members of the community are invited to provide constructive feedback or ask specific technical questions regarding the implementation of the preprocessing script or the training parameters used in this tutorial.

Introduction

Artificial intelligence models rely heavily on algorithms to function efficiently. One of the most powerful object detection frameworks today is YOLOv8. A common question developers ask is: Which algorithm does YOLOv8 use?

To understand the answer, we first need clarity on what algorithms are, how they work in computing, and how they are applied in AI systems like YOLOv8. This article explains the core YOLOv8 algorithm in a structured and easy-to-understand way.

What Does Algorithm Mean in Computing?

Algorithm does yolov8 use is a step-by-step set of instructions for solving a problem or performing a task. In computer science, algorithms process input data and generate meaningful output.

When people ask:

What are algorithms in programming?

What are algorithms in computer science?

What does an algorithm mean in computing?

They refer to the logical procedures that enable software to function. Algorithms are the foundation of search engines, navigation apps, recommendation systems, and artificial intelligence models.

What Are Algorithms in AI?

In artificial intelligence, algorithms allow machines to learn patterns from data and make predictions. These include machine learning algorithms, deep learning architectures, and optimization techniques.

When we ask what algorithms in AI are, we are referring to neural networks, classification systems, regression models, and object detection frameworks. YOLOv8 belongs to this AI category and is specifically designed for computer vision tasks.

The Core YOLOv8 Algorithm

YOLOv8 is a deep learning object detection algorithm based on Convolutional Neural Networks (CNNs). It follows a single-stage detection approach, meaning it predicts object locations and classifications in one pass through the network.

The YOLOv8 algorithm includes:

A backbone network for feature extraction

A neck structure for feature aggregation

A detection head for bounding box and class prediction

Anchor-free detection design for improved accuracy

Unlike traditional two-stage detectors, YOLOv8 processes images faster while maintaining high precision.

How Does the YOLOv8 Algorithm Work?

If someone asks, how do algorithms work?, YOLOv8 provides a practical example.

Here is how the algorithm functions step by step:

The input image is passed into the neural network.

The backbone extracts important visual features.

The detection head predicts bounding boxes and object classes.

Non-Maximum Suppression removes duplicate detections.

Final results display object labels and confidence scores.

This structured pipeline demonstrates how a real-world AI algorithm operates.

Algorithm Analysis and Performance

Algorithm analysis evaluates efficiency and performance. When discussing what algorithm analysis is, we consider:

Computational complexity

Inference speed

Model size

Accuracy metrics such as mean Average Precision (mAP)

YOLOv8 improves performance over earlier YOLO versions by optimizing the model architecture and reducing unnecessary computations. This makes it suitable for real-time applications.

What Are Algorithms Used For in Computer Vision?

Algorithms in computer vision are used for:

Object detection

Image classification

Image segmentation

Pose estimation

YOLOv8 is widely applied in surveillance systems, autonomous vehicles, medical imaging, and retail analytics. When asking what algorithms are used for?, YOLOv8 demonstrates how it enables machines to interpret and understand visual data.

Comparison with Other Algorithm Types

Not all algorithms serve the same purpose. For example:

Mathematical algorithms solve numerical problems.

Social media algorithms rank and recommend content.

Navigation systems like Google Maps use pathfinding algorithms such as Dijkstra’s or A*.

Genetic algorithms are used for evolutionary optimization problems.

However, the YOLOv8 algorithm is specifically built for deep learning-based object detection, not navigation, trading, or social ranking systems.

How to Use the YOLOv8 Algorithm

Using YOLOv8 involves a clear implementation process:

Install the YOLOv8 framework.

Load a pretrained model or train a custom dataset.

Provide images or videos as input.

Run inference to detect objects.

Evaluate and fine-tune results.

This answers practical questions like, "How do you use an algorithm?" and demonstrates real-world AI deployment.

Conclusion

The YOLOv8 algorithm is a deep learning-based object detection system built on convolutional neural networks and a single-stage detection architecture. It processes images efficiently, predicts object locations accurately, and performs in real time.

Understanding what algorithms are, how they work in computing, and how they are applied in AI helps clarify why YOLOv8 is both powerful and efficient. With optimized performance, structured architecture, and advanced detection mechanisms, YOLOv8 stands as one of the leading object detection algorithms in modern computer vision.

🚀 Edge AI Made Easy with OK3576-C & YOLOv8!

With the rapid growth of intelligent vision applications, real-time on-device object detection has become a core capability for industrial vision, smart devices, and surveillance.

At Forlinx Embedded, we’ve demonstrated a full workflow from model to edge deployment using the OK3576-C board with RK3576 NPU:

1️⃣ Convert YOLOv8 pre-trained models (.pt) → RKNN format 2️⃣ Deploy the model on the OK3576-C board 3️⃣ Run real-time object detection using a USB camera (~30 FPS)