What is Object Detection in Computer Vision?

Last Updated : 26 Jun, 2026

Object Detection is a computer vision task that identifies and locates multiple objects within an image or video. Unlike image classification, which assigns a label to an entire image, object detection predicts both the object category and its location using bounding boxes.

  • Enables real-time analysis of images and video streams.
  • Forms the foundation of applications such as autonomous driving, surveillance, and robotics.
computer_vision_object_recognition

Types

  1. Image Classification: Assigns a single label to the entire image based on its content. It determines what is present in the image but does not indicate the object's location.
  2. Object Localization: Identifies an object and determines its position within the image by drawing a bounding box around it.
  3. Object Detection: Combines image classification and localization to identify multiple objects in an image, assign labels to them, and provide their locations using bounding boxes.

Working

  1. Input Image: The process begins with an input image or video frame.
  2. Pre-processing: The image is resized, normalized, or transformed into a suitable format for the model.
  3. Feature Extraction: Important visual features are extracted to identify object patterns.
  4. Classification: Detected regions are classified into predefined object categories.
  5. Localization: Bounding boxes are generated to determine the location of each object.
  6. Non-Maximum Suppression (NMS): Overlapping bounding boxes are filtered to retain the most accurate detections.
  7. Output: The final image is displayed with labeled bounding boxes around the detected objects.

Deep Learning Methods for Object Detection

Object detection methods are broadly classified into two categories:

1. Two-Stage Detectors

These methods first generate potential object regions and then classify them.

  • R-CNN: Uses selective search to generate region proposals and classifies each region using a CNN.
  • Fast R-CNN: Processes the entire image once and uses ROI pooling for classification and localization.
  • Faster R-CNN: Introduces a Region Proposal Network (RPN) for faster and more accurate region generation.

Single-Stage Detectors

These methods perform object localization and classification in a single pass, making them faster.

Implementation

Let's consider an object detection task where YOLOv8 is used to identify and localize objects in an image.

Step 1: Install Ultralytics

Installing Ultralytics.

Python 
!pip install ultralytics

Step 2: Import Packages

Importing the packages like YOLO and CV2.

Python 
from ultralytics import YOLO
import cv2

Step 3: Load a Pretrained Model

Loading a pre trained YOLOv8 model.

Python 
model = YOLO('yolov8s.pt')

Step 4: Run Object Detection

Providing image path for object detection.

Python 
results = model('your-image-path.jpg')

Step 5: Save the Result

Saving the result with bounding boxes.

Python 
results[0].save(filename='output.jpg')

Step 6: Display the Image

Displaying the image with bounding boxes.

Python 
img = cv2.imread('output.jpg')
cv2.imshow("Object Detection Result", img)
cv2.waitKey(0)
cv2.destroyAllWindows()

Output:

You can download the source code from here.

Applications

  1. Autonomous Vehicles: It can detect pedestrians other vehicles and obstacles and make real-time decisions to ensure safe navigation.
  2. Security and Surveillance: It enhances security systems by enabling the identification of suspicious activities, intruders and overall surveillance efficiency.
  3. Healthcare: It assists in medical imaging, helping to detect abnormalities such as tumors in X-rays and MRIs thus contributing to accurate and timely diagnoses.
  4. Retail: It automates inventory management, prevents theft and analyzes customer behavior enhancing operational efficiency and customer experience.
  5. Robotics: It enables robots to interact with their environment, recognize objects and perform tasks autonomously enhancing their functionality.
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