10 Computer Vision Projects Ideas For Beginners

Computer vision is a branch of artificial intelligence that enables and trains computers to understand the visual world. This article brings for you exciting computer vision projects.This article will tell you what is computer vision and why you should try computer vision projects.

Computer vision deals with how computer systems extract significant statistics from photographs or videos. It has a wide variety of applications, such as opposite engineering, safety inspections, photograph enhancement and processing, pc animation, self-reliant navigation, and robotics. 

In this article, we’ll discover good OpenCV projects, from beginner-stage to expert-stage. For every project, you’ll see the crucial guides, supply codes, and datasets so that you can get directly to paintings on them in case you want. This post will cover intriguing computer vision applications that a beginner can use.

Table of contents

• What is Computer vision?
• Why to try Computer vision projects
  • Computer vision projects
  • Pedestrian detection
  • Contour & Edge Detection
  • Traffic light detection
  • Face mask detection
  • Text Recognition using OpenCV
  • Face Recognition 
  • Object Detection
  • Road Lane Detection in Autonomous Vehicles
  • Multi-Class Image Classification
  • Human pose estimation
• Conclusion

What is Computer vision?

Computer vision is the discipline of computer systems figuring out how to analyze digital photographs and videos. Sensors, such as cameras, recognize features in images, like shapes and colors. Algorithms then use these features to compute statistics, like the area of an object, the object’s movement, or the photograph’s age. Computer vision is frequently used in robotics and video analysis. However, it might also be employed in applications like self-reliant navigation, computer animation, or opposite engineering.

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Why to try Computer vision projects

Computer vision projects for beginners are an excellent approach to discovering the fundamentals of computer vision. Even if you’re new to the discipline, you’ll discover lots to examine and use computer vision projects. Beginning with different projects and discovering the best way to research and create can lead to your self-resilience as you practice and develop your talent. Computer vision projects are also an excellent way to discover the profession. Computer vision projects for learners are an exciting approach to teach them about engineering and how to examine concepts and make observations.

Computer vision is a field that’s advancing as rapidly as our technology, so a software program engineer’s occupation is likely in demand.

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Computer vision projects

1. Pedestrian detection

Creating an object detection model that detects pedestrians is one of his easiest and fastest computer vision projects. You can use one of our free image annotation tools or try V7.

Pedestrian detectors are widely used in the automotive industry for road safety, human-robot interaction, and intelligent video systems.

2. Contour & Edge Detection

This edge detection project is a wonderful place to start if you’re new to computer vision. Applications for CV first find edges before gathering further data. The Canny edge detector is the most well-known edge detection technique because it performs very well compared to other edge detectors. It is also a problematic edge-detection method. 

Contours are lines connecting consecutive objects or points (along their boundaries) with the same color or intensity. For example, if we have a leaf in the image, it recognizes leaf shapes based on parameters and edges. Contours are essential tools for recognizing shapes and objects. The outline of an object is the border that makes up the object’s shape. Contours are also called outlines, edges, or textures. These are ways to mark changes in depth.

3. Traffic light detection

Traffic light detection has numerous uses in intelligent transportation, including well-known ones like autonomous vehicles and smart cities. Traffic light recognition is important in advanced driver assistance and autonomous vehicle systems, ensuring timely and appropriate responses to traffic lights (TL) within cross sections. Detect traffic lights using a pre-trained neural network and perform their detection through another neural network trained to discriminate colors.

4. Face mask detection

Masks are one of the few preventive measures against COVID-19 without vaccination and play an important role in protecting people’s health from respiratory diseases. Scientists have tried to build an automatic face mask detection system to ensure the use of face masks in public places. After the COVID-19 outbreak, other researchers developed a unique technique to monitor face masks in public areas. Surveillance systems are used to monitor public spaces using image processing algorithms to ensure no one is seen in crowded areas (2). Deep learning-based approaches to object identification and image analysis have grown in popularity over the last few years. Most of the research has been done using convolutional neural network models. There are two cases where the current face mask detection algorithm cannot reliably identify a mask. Accurate identification of all “masked and unmasked” faces is difficult when many people are in one image or video frame. In our country, women wear a half-face veil that serves the same purpose as a face mask but is not identified as a face mask by current methods.

5. Text Recognition using OpenCV

OpenCV offers an extensive library of functions that can be used for computer vision tasks. They are organized into the following categories: face detection, face recognition, image grab (e.g., real-time image grab), image processing, image transformation, image grab (e.g., real-time image grab), image grabbing, image segmentation, and object detection.

Also read: OpenCV python: Beginner’s guide 

6. Face Recognition 

OpenCV offers an extensive library of functions that can be used for computer vision tasks. They are organized into the following categories: face detection, face recognition, image grab (e.g., real-time image grab), image processing, image transformation, image grabbing, image segmentation, and object detection.

7. Object Detection

Computer vision is used to make autonomous systems that detect, track, and recognize objects. Computer vision is also being used to make systems that can understand human-generated data like images, video, and sound. Computer vision is used to make autonomous systems that detect, track, and recognize objects. Computer vision is also being used to make systems that can understand human-generated data like images, video, and sound.

8. Road Lane Detection in Autonomous Vehicles

Advanced Driving Assistance Systems and intelligent and autonomous vehicles are promising solutions for improving traffic congestion, road safety, and passenger comfort. Such applications call for sophisticated computer vision algorithms, which necessitate strong computers with fast processing capacity. The primary goal is reliable navigation, which frequently relies on system vision to gather RGB images of the road for more sophisticated processing.

Additionally, a lane detection system is needed. The purpose of the lane detection problem is to identify the pixels that belong to the lane class. This problem is often stated as a semantic or instance segmentation problem.

9. Multi-Class Image Classification

Image classification, or computer vision, is a branch of artificial intelligence concerned with designing systems that recognize or classify objects based on digital images. It is a popular field due to its wide range of applications. Image classification can be used for various applications, such as identifying specific flowers in photographs or classifying medical images as normal or diseased.

10. Human pose estimation

An intriguing use of computer vision is human pose estimation. Postnet, an open-source model for estimating human poses, must be familiar to you. Pose estimation is, in essence, a computer vision technique that determines the pose of a person or object in an image or video.

Let’s first understand the “Human Pose Skeleton” before discussing how to pose estimation functions. It is the collection of coordinates that describe a person’s stance. A limb is a pair of coordinates. Additionally, posture estimation is carried out by discovering, tracking, and recognizing the crucial areas of the human stance skeleton in an image or video.

Conclusion

Computer Vision is one of the most critical fields in computer science. In this field, computers can see and understand the world around us, which is why computer vision applications are so diverse. From self-driving cars to virtual assistants that can understand human language and interact with people, computer vision is an essential part of the future. The computer vision projects in this article will show you how to use computer vision techniques to accomplish various tasks.