✨ Training YOLOv12 for Detecting Pothole Severity Using a Custom Dataset ✨

📖 Table of Contents Training YOLOv12 for Detecting Pothole Severity Using a Custom Dataset Introduction Dataset and Task Overview About the Dataset What Are We Detecting? Defining Pothole Severity Can the Pothole Severity Logic Be Improved? Configuring Your Development Environment Training…...

🏷️ #ComputerVision #DeepLearning #ObjectDetection #Tutorial #YOLO

✨ Training YOLOv12 for Detecting Pothole Severity Using a Custom Dataset ✨

📖 Table of Contents Training YOLOv12 for Detecting Pothole Severity Using a Custom Dataset Introduction Dataset and Task Overview About the Dataset What Are We Detecting? Defining Pothole Severity Can the Pothole Severity Logic Be Improved? Configuring Your Development Environment Training…...

🏷️ #ComputerVision #DeepLearning #ObjectDetection #Tutorial #YOLO

✨ OpenCV Social Distancing Detector ✨

📖 In this tutorial, you will learn how to implement a COVID-19 social distancing detector using OpenCV, Deep Learning, and Computer Vision. Today’s tutorial is inspired by PyImageSearch reader Min-Jun, who emailed in asking: Hi Adrian, I’ve seen a number of…...

🏷️ #DeepLearning #MedicalComputerVision #ObjectDetection #Tutorials

✨ COVID-19: Face Mask Detector with OpenCV, Keras/TensorFlow, and Deep Learning ✨

📖 In this tutorial, you will learn how to train a COVID-19 face mask detector on a custom dataset with OpenCV, Keras/TensorFlow, and Deep Learning. Last month, I authored a blog post on detecting COVID-19 in X-ray images using deep learning.…...

🏷️ #DeepLearning #FaceApplications #KerasandTensorFlow #MedicalComputerVision #ObjectDetection #Tutorials

✨ Training YOLOv12 for Detecting Pothole Severity Using a Custom Dataset ✨

📖 Table of Contents Training YOLOv12 for Detecting Pothole Severity Using a Custom Dataset Introduction Dataset and Task Overview About the Dataset What Are We Detecting? Defining Pothole Severity Can the Pothole Severity Logic Be Improved? Configuring Your Development Environment Training…...

🏷️ #ComputerVision #DeepLearning #ObjectDetection #Tutorial #YOLO

✨ People Tracker with YOLOv12 and Centroid Tracker ✨

📖 Table of Contents People Tracker with YOLOv12 and Centroid Tracker Introduction Why People Tracker Monitoring Matters How YOLOv12 Enables Real-Time Applications Configuring Your Development Environment Downloading the Input Video Install gdown Download the Video Visualizing the Inference and Trackin...

🏷️ #ComputerVision #ObjectDetection #PeopleTracker #Tutorial #YOLOv12

✨ Object Detection and Visual Grounding with Qwen 2.5 ✨

📖 Table of Contents Object Detection and Visual Grounding with Qwen 2.5 Introduction and Types of Spatial Understanding Object Detection Visual Grounding and Counting Understanding Relationships How Spatial Understanding Works in Qwen 2.5 VL Models Prompt Structure Task-Specific Instruction Object or…...

🏷️ #ObjectDetection #Qwen25 #Qwen25 #Tutorial #VisualGrounding

✨ AI for Healthcare: Fine-Tuning Google’s PaliGemma 2 for Brain Tumor Detection ✨

📖 Table of Contents AI for Healthcare: Fine-Tuning Google’s PaliGemma 2 for Brain Tumor Detection Configuring Your Development Environment Setup and Imports Load the Brain Tumor Dataset Format Dataset to PaliGemma Format Display Train Image and Label COCO Format BBox to…...

🏷️ #FineTuning #ObjectDetection #PaliGemma2 #PEFT #QLoRA #Transformers #Tutorial #VisionLanguageModels

✨ Object Tracking with YOLOv8 and Python ✨

📖 Table of Contents Object Tracking with YOLOv8 and Python YOLOv8: Reliable Object Detection and Tracking Understanding YOLOv8 Architecture Mosaic Data Augmentation Anchor-Free Detection C2f (Coarse-to-Fine) Module Decoupled Head Loss Object Detection and Tracking with YOLOv8 Object Detection Object T...

🏷️ #AdvancedComputerVision #DataScience #DeepLearning #MachineLearning #ObjectDetection #ObjectTracking #ProgrammingTutorials #Tutorial #VideoObjectTracking #YOLO

✨ OpenCV Vehicle Detection, Tracking, and Speed Estimation ✨

📖 In this tutorial, you will learn how to use OpenCV and Deep Learning to detect vehicles in video streams, track them, and apply speed estimation to detect the MPH/KPH of the moving vehicle. This tutorial is inspired by PyImageSearch readers…...

🏷️ #EmbeddedIoTandComputerVision #IoT #Movidius #ObjectDetection #ObjectTracking #RaspberryPi #Tutorials

✨ Thermal Vision: Night Object Detection with PyTorch and YOLOv5 (real project) ✨

📖 Table of Contents Thermal Vision: Night Object Detection with PyTorch and YOLOv5 (real project) Object Detection with Deep Learning Through PyTorch and YOLOv5 Discovering FLIR Thermal Starter Dataset Thermal Object Detection Using PyTorch and YOLOv5 Configuring Your Development Environment Having…...

🏷️ #InfraredVision #IRVision #ObjectDetection #OpenCVTutorials #Tutorials #YOLOv5

✨ YOLO and Tiny-YOLO object detection on the Raspberry Pi and Movidius NCS ✨

📖 In this tutorial, you will learn how to utilize YOLO and Tiny-YOLO for near real-time object detection on the Raspberry Pi with a Movidius NCS. The YOLO object detector is often cited as being one of the fastest deep learning-based…...

🏷️ #DeepLearning #EmbeddedIoTandComputerVision #Movidius #ObjectDetection #RaspberryPi #Tutorials

✨ OpenCV Vehicle Detection, Tracking, and Speed Estimation ✨

📖 In this tutorial, you will learn how to use OpenCV and Deep Learning to detect vehicles in video streams, track them, and apply speed estimation to detect the MPH/KPH of the moving vehicle. This tutorial is inspired by PyImageSearch readers…...

🏷️ #EmbeddedIoTandComputerVision #IoT #Movidius #ObjectDetection #ObjectTracking #RaspberryPi #Tutorials

#YOLOv8 #ComputerVision #ObjectDetection #IndustrialAI #Python

Applying YOLOv8 for Industrial Automation: Counting Plastic Bottles

This lesson will guide you through a complete computer vision project using YOLOv8. The goal is to detect and count plastic bottles in an image from an industrial setting, such as a conveyor belt or a storage area.

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Step 1: Setup and Installation

First, we need to install the necessary libraries. The ultralytics library provides the YOLOv8 model, and opencv-python is essential for image processing tasks.

#Setup #Installation

# Open your terminal or command prompt and run this command:
pip install ultralytics opencv-python

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Step 2: Loading the Model and the Target Image

We will load a pre-trained YOLOv8 model. These models are trained on the large COCO dataset, which already knows how to identify common objects like 'bottle'. Then, we'll load our industrial image. Ensure you have an image named factory_bottles.jpg in your project folder.

#ModelLoading #DataHandling

import cv2
from ultralytics import YOLO

# Load a pre-trained YOLOv8 model (yolov8n.pt is the smallest and fastest)
model = YOLO('yolov8n.pt')

# Load the image from the industrial setting
image_path = 'factory_bottles.jpg' # Make sure this image is in your directory
img = cv2.imread(image_path)

# A quick check to ensure the image was loaded correctly
if img is None:
    print(f"Error: Could not load image at {image_path}")
else:
    print("YOLOv8 model and image loaded successfully.")

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Step 3: Performing Detection on the Image

With the model and image loaded, we can now run the detection. The ultralytics library makes this process incredibly simple. The model will analyze the image and identify all the objects it recognizes.

#Inference #ObjectDetection

# Run the model on the image to get detection results
results = model(img)

print("Detection complete. Processing results...")

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Step 4: Filtering and Counting the Bottles

The model detects many types of objects. Our task is to go through the results, filter for only the 'bottle' class, and count how many there are. We'll also store the locations (bounding boxes) of each detected bottle for visualization.

#DataProcessing #Filtering

# Initialize a counter for the bottles
bottle_count = 0
bottle_boxes = []

# The model's results is a list, so we loop through it
for result in results:
    # Each result has a 'boxes' attribute with the detections
    boxes = result.boxes
    for box in boxes:
        # Get the class ID of the detected object
        class_id = int(box.cls)
        # Check if the class name is 'bottle'
        if model.names[class_id] == 'bottle':
            bottle_count += 1
            # Store the bounding box coordinates (x1, y1, x2, y2)
            bottle_boxes.append(box.xyxy[0])

print(f"Total plastic bottles detected: {bottle_count}")

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Step 5: Visualizing the Results

A number is good, but seeing what the model detected is better. We will draw the bounding boxes and the final count directly onto the image to create a clear visual output.

#Visualization #OpenCV