Object Detection Techniques
Object detection is a fundamental problem in computer vision that involves identifying and localizing objects within an image or video stream. This section will cover various object detection techniques, showcasing their methodologies, applications, and Python implementations.
1. Introduction to Object Detection
Object detection differs from image classification, where the task is only to classify an image into a category. In contrast, object detection identifies multiple objects in an image and provides their locations usually in the form of bounding boxes.2. Traditional Object Detection Techniques
Before the advent of deep learning, traditional techniques were commonly used for object detection. Here are a few:2.1. Haar Cascades
Haar cascades use machine learning to create a classifier that can identify objects in images.Example:
`python
import cv2
Load the cascade
face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')Read the input image
img = cv2.imread('person.jpg') gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)Detect faces
faces = face_cascade.detectMultiScale(gray, 1.1, 4)Draw bounding boxes
for (x, y, w, h) in faces: cv2.rectangle(img, (x, y), (x + w, y + h), (255, 0, 0), 2)Display the output
cv2.imshow('img', img) cv2.waitKey()` 2.2. HOG + SVM
Histogram of Oriented Gradients (HOG) features combined with Support Vector Machines (SVM) is another traditional method for detecting objects.3. Modern Object Detection Techniques
With the rise of deep learning, several new techniques have emerged:3.1. YOLO (You Only Look Once)
YOLO is a state-of-the-art, real-time object detection system that divides the image into a grid and predicts bounding boxes and probabilities for each grid cell.Example:
`python
import cv2
import numpy as np
Load YOLO
net = cv2.dnn.readNet('yolov3.weights', 'yolov3.cfg') layer_names = net.getLayerNames() output_layers = [layer_names[i[0] - 1] for i in net.getUnconnectedOutLayers()]Read the image
img = cv2.imread('image.jpg') height, width, channels = img.shapeDetecting objects
blob = cv2.dnn.blobFromImage(img, 0.00392, (416, 416), (0, 0, 0), True, crop=False) net.setInput(blob) outs = net.forward(output_layers)Process the detections
for out in outs: for detection in out: scores = detection[5:] class_id = np.argmax(scores) confidence = scores[class_id] if confidence > 0.5:Object detected
center_x = int(detection[0] * width) center_y = int(detection[1] * height) w = int(detection[2] * width) h = int(detection[3] * height)
Rectangle coordinates
x = int(center_x - w / 2) y = int(center_y - h / 2)cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), 2)
Show output
cv2.imshow('Image', img) cv2.waitKey(0)`