Computer Vision Final Year Projects with Source Code

Byadmin Final Year Projects

Computer Vision Final Year Projects for BE, BTech, ME, MSc, MCA and MTech final year engineering students. These Computer Vision projects give practical experience and help complete final-year submissions. All projects follow IEEE standards and each project includes source code, project thesis report, presentation, project execution and explanation.

Computer Vision Final Year Projects

  1. A Swin Transformer-Based Approach for Motorcycle Helmet Detection
    This project focuses on automatically detecting whether motorcyclists are wearing helmets using video cameras. It uses advanced computer vision and machine learning techniques to identify helmets even in complex situations and with multiple passengers. The method combines a transformer model with other neural network tools to improve accuracy. Tests show that it works better than existing detection systems.
  2. Identification of Emotions From Facial Gestures in a Teaching Environment With the Use of Machine Learning Techniques
    This project uses computer vision and machine learning to understand students’ emotions in a classroom. It tracks facial gestures to identify feelings like interest, boredom, or enthusiasm during learning. The system builds a database of real, spontaneous emotions and helps teachers evaluate students’ emotional engagement along with their learning progress. It focuses on supporting teachers in face-to-face education.
  3. Internet of Things and Deep Learning Enabled Diabetic Retinopathy Diagnosis Using Retinal Fundus Images
    This project develops a smart system to detect diabetic eye disease early using Internet-connected devices and deep learning. Eye images are collected with IoT devices and sent to the cloud for processing. The system cleans the images, highlights damaged regions, extracts important features, and uses an AI model to classify the disease. Tests show this method is more accurate and effective than earlier approaches.
  4. Multi-View Computed Tomography Network for Osteoporosis Classification
    This study focuses on detecting early bone loss conditions, like osteopenia and osteoporosis, from CT scans. The researchers developed a new deep learning model called MVCTNet, which uses two images from a CT scan to automatically identify these conditions. Their approach avoids manual image cropping and improves accuracy compared to previous methods. Tests on nearly 3,000 patients’ CT images show that the model performs well and could help with earlier and easier diagnosis.
  5. A Deep Learning-Based Efficient Firearms Monitoring Technique for Building Secure Smart Cities
    This project focuses on automatically detecting guns and human faces in videos and images using deep learning. It combines multiple detection methods to improve accuracy. The system can help police quickly identify violent incidents and monitor social media for gun-related content. It works reliably and performs better than single detection models.
  6. A Novel Spatio Temporal Deep Learning Vehicle Turns Detection Scheme Using GPS-Only Data
    This project focuses on improving driver assistance systems using only GPS data. The researchers developed a method to turn GPS trajectories into images and trained a neural network to analyze them. The system can accurately detect when a car is turning or going straight. It works better than existing methods and helps make driving safer and smarter.
  7. A Smart Leaf Blow Robot Based on Deep Learning Model
    This project created a robot that can automatically collect fallen leaves. It uses a camera and a computer program to recognize leaves on the ground. The robot moves on wheels and directs a blower to gather the leaves into a bag. The system works in real time and can handle different types of leaves without human help.
  8. Automated Red Palm Weevil Detection Using Gorilla Troops Optimizer With Deep Learning Model
    This project develops an automated system to detect Red Palm Weevil, a harmful pest for palm trees. It uses artificial intelligence and computer vision to analyze images and identify infected trees accurately. The system combines deep learning with an optimization algorithm to improve detection speed and accuracy. Tests show it can detect the pest with over 99% accuracy, helping protect plantations efficiently.
  9. Generalization of Forgery Detection With Meta Deepfake Detection Model
    This project focuses on detecting fake videos and images created by face manipulation. It uses a deep learning approach that can learn from multiple sources and adapt to new, unseen types of fake media. The model trains in a way that improves its ability to generalize, so it can detect deepfakes without needing updates for each new type. Overall, it aims to make face forgery detection more reliable in real-world situations.
  10. Modified Earthworm Optimization With Deep Learning Assisted Emotion Recognition for Human Computer Interface
    This project focuses on teaching computers to recognize human emotions from facial expressions. It uses a deep learning model to extract features from faces and an optimization method to improve accuracy. A special algorithm then identifies and classifies the emotions. Tests show that this approach works very well, reaching nearly 99% accuracy.
  11. Reverse Image Search for Collage A Novel Local Feature-Based Framework
    This research focuses on finding specific images in large collections of collages, like those shared on social media. The study uses computer algorithms to extract important parts of an image and compare them to other images. The proposed method can find exact matches or slightly changed versions of an image. It works well on standard datasets, achieving high accuracy, especially using the SIFT algorithm.
  12. SIGNFORMER DeepVision Transformer for Sign Language Recognition
    This project focuses on helping people communicate with the hearing impaired by recognizing sign language automatically. The researchers used a vision transformer model to identify static Indian sign language gestures. Their method breaks hand gestures into small parts and analyzes them using a transformer network. The system is accurate, fast to train, and performs better than previous approaches.
  13. Artificial Intelligence in Cosmetic Dermatology A Systematic Literature Review
    This project reviews how artificial intelligence (AI) is being used in cosmetic dermatology. It looks at studies from 2018 to 2023 and organizes them into areas like product development, skin assessment, diagnosis, treatment advice, and predicting results. The study helps researchers understand trends in AI applications and provides guidance for doctors and practitioners in improving cosmetic skin treatments. It highlights both current achievements and opportunities for future research in aesthetic medicine.
  14. Artificial Intelligence Technology in the Agricultural Sector A Systematic Literature Review
    This project explores how artificial intelligence is changing farming. It looks at tools like smart sensors, robots, and data analysis to monitor crops, soil, and water use. The study shows how AI can help farmers grow better crops, use resources efficiently, and increase profits. It also examines the benefits, challenges, and different AI methods used in modern agriculture.
  15. Dynamic Gesture Recognition Based on Three-Stream Coordinate Attention Network and Knowledge Distillation
    This project focuses on recognizing hand gestures from videos more accurately and quickly. It uses a new method called 3SCKI that helps the system focus on gestures and ignore background distractions. The model learns efficiently from existing data and can even recognize gestures it has not seen before. Tests show it performs very well on a large gesture dataset for different types of video data.
  16. Enhanced Artificial Vision for Visually Impaired Using Visual Implants
    This project aims to help visually impaired people see better using retinal implants. It uses a camera on glasses to detect people nearby, their age, gender, emotions, and distance. This information is then converted into simple visual patterns that the implant can show to the user. The system works on a small, low-cost device and can run in real time, making it practical for daily use.
  17. Recurrent Residual Networks Contain Stronger Lottery Tickets
    This project shows that large neural networks can be simplified without training by selecting smaller subnetworks from them. These small networks are sparse, use fewer values, and can run efficiently on hardware. The study finds that converting some networks into recurrent forms improves accuracy and reduces memory use. Using this method, a popular network can be shrunk almost 50 times while keeping good performance.
  18. Internet of Things and Deep Learning Enabled Diabetic Retinopathy Diagnosis Using Retinal Fundus Images
    This project develops a smart system to detect diabetic retinopathy, a disease that can cause blindness in diabetic patients. It uses IoT devices to collect eye images and sends them to the cloud for analysis. The system cleans and enhances the images, identifies damaged regions, and uses advanced deep learning methods to accurately diagnose the disease. This approach aims to help doctors detect the condition early and improve healthcare outcomes.
  19. Water Classification Using Convolutional Neural Network
    This project focuses on classifying different water sources using images. The researchers improved the images’ quality using special techniques to make textures and contrasts clearer. They then used a new neural network called WaterNet to identify the water types. Their method achieved 97% accuracy and performed better than existing popular models.
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Computer Vision Project Synopsis & Presentation

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