Top 5 Image Processing Projects Ideas & Topics [For Beginners]

We'll go over an overview of image processing in this blog before diving into a few project ideas that centre around it. Picture processing is a method of extracting valuable information from an image by performing operations on it. After applying a few processes to an image, we will acquire an updated image or particular qualities associated with these photos. In image processing, an image is a two-dimensional array of values ranging from 0 to 255.

Image compression, sharpening, and edge detection are all achieved using certain filters and operators that transform the original image into the desired output. When brightening an image, for example, the operator or filter will function in such a way that the image's pixel value increases. These operators perform mathematical operations on a 2-D array and provide a new set of output arrays containing the required result.

These approaches are heavily used in computer vision, artificial intelligence, and machine learning. Let's look at some of the project ideas that may be produced using the aforementioned image processing approach now that we have a basic understanding of what it is.

Also Read: Computer Vision and Image Processing Fundamentals and Applications Course

Top Image Processing Project Ideas

1. Social Distancing is being monitored

With the spread of COVID-19 over the world, maintaining social distance while travelling in public settings is more vital than ever. In this case, image processing can be a game-changer. We'll finish the job by gathering data from CCTV cameras and analysing one frame at a time. To begin, we use morphological procedures and detection algorithms to identify pedestrians in a frame. Then, for each pedestrian, we draw a bounding box around them. The distance between a pedestrian's bounding box and the bounding boxes surrounding it is then determined. Then, based on the distance between the bounding boxes, we determine whether the pedestrians in the frame are red, yellow, or green.

2. Detecting Masks

Masks have been required since the outbreak was discovered. In order to prevent further COVID outbreaks, mask detection is equally as important as social separation. We must first recognise a human face in order to detect a mask. Face landmarks such as the eyes, nose, and mouth can be used to do this. We need to design an algorithm that can recognise the difference between a face with and without a mask after we've detected faces. This necessitates the application of a deep learning algorithm. To train a deep learning model, use datasets with both mask and non-mask pictures. After training, the model will be able to tell the difference between people wearing masks and those who aren't.

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3. Detection of Lanes and Curves

Self-driving cars are the way of the future. Many companies are aggressively investing in autonomous vehicle research and development in order to decrease human involvement and the danger that comes with it. We use photo segmentation for filtering and edge detection with a deep learning model to determine the presence of lanes and their direction.

A step-by-step procedure would look like this.

• Input from video frames.

• Each frame is converted to the grayscale image that corresponds to it.

• Filters are employed to reduce the quantity of noise.

• Detecting edges with a sophisticated edge detector.

• Locating the lane coordinates on the road.

• Deep learning is being utilised to improve the detection of lanes and their orientation.

4. Drivers' Drowsiness Detection

Because of the high incidence of accidents caused by drivers who are not fully alert, the need for drowsiness detection in automobiles is critical. A tiredness detection system can alert the driver if it detects a possible lack of alertness in the driver's eye. By recognising and analysing eye patterns, this device can alert the driver and avert accidents. To begin, separate the eye area of the face from the rest of the face by locating and segmenting it.

5. Recognition of License Plates

Yes, you read that correctly: we can detect licence plates automatically. Traffic cops are no longer required to manually record the licence numbers of vehicles that violate the rules. Because to advancements in the field of image processing, such a task is now possible. One of the steps required for licence plate detection is the use of appropriate filters to minimise noise in the input image before performing morphological operations on it. We also employ a technology called Optical Character Recognition (OCR) to extract text from pictures in the licence plate zone of interest.

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Conclusion

We've seen five examples of how image processing can be utilised to solve a problem up to this point. Let me assure you, however, that image processing has penetrated virtually every business and is currently employed in almost every field, either directly or indirectly. Because it employs Python as its programming language, it is straightforward to use and comprehend. This article will give you an overview of image processing as well as a few examples of projects that are linked to it. However, we encourage you to look into more pressing issues that can be solved using image processing techniques. To summarise, developing image processing algorithms requires skill and, once mastered, can help you advance quickly in your career while addressing real-world problems.