Semantic Segmentation Final Year Projects with Source Code

Byadmin Final Year Projects

Semantic Segmentation Final Year Projects for BE, BTech, ME, MSc, MCA and MTech final year engineering students. These Semantic Segmentation 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.

Semantic Segmentation Final Year Projects

  1. A VAN-Based Multi-Scale Cross-Attention Mechanism for Skin Lesion Segmentation Network
    This project focuses on improving medical image segmentation, which helps computers identify regions like tumors in medical scans. Traditional methods using neural networks struggle to capture both small details and overall structures. The researchers combined ideas from Transformers and visual attention networks to create a new model called M-VAN Unet. This model uses special attention methods to better learn detailed and global features, and experiments show it performs better than existing methods.
  2. EMED-UNet An Efficient Multi-Encoder-Decoder Based UNet for Medical Image Segmentation
    This project improves medical image segmentation by making the popular U-Net model faster and lighter. The researchers created a new version called EMED-UNet, which uses multiple encoders and decoders to capture features more effectively. It works well on different medical imaging datasets while using much less memory and computing power. Overall, it is accurate, efficient, and easier to deploy in real-time applications.
  3. BSANet High-Performance 3D Medical Image Segmentation
    This project focuses on improving medical image analysis, especially for tasks like brain tumor and organ segmentation. It introduces BSANet, a 3D network that can better understand images by focusing on important areas and combining information at different scales. This helps the system capture more details and make more accurate predictions. The model is tested on standard medical datasets and shows strong performance.
  4. Intelligent Deployment Solution for Tabling Adapting Deep Learning
    This project develops a smart system to improve mineral processing. It uses deep learning to analyze images and identify features of mineral ore belts. Then, it predicts how operating conditions affect these minerals using an advanced regression model. The system makes processing faster and more accurate, offering new possibilities for research.
  5. Recognition of the Shape and Location of Multiple Power Lines Based on Deep Learning With Post-Processing
    This project focuses on making drone flights safer by detecting power lines. It uses a deep learning method called YOLO to find power lines in different shapes and positions. The algorithm improves detection by fixing missed lines and removing false ones. Tests showed it works better than older methods and can detect power lines in real time while the drone flies.
  6. Pixel Difference Unmixing Feature Networks for Edge Detection
    This project builds a new deep learning model that detects edges in images. It uses fewer parameters, so it needs less memory and computing power. The model learns important details by combining information from different scales and improving how features are separated. Experiments show that it works better than many existing small models and performs almost as well as large models.
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Semantic Segmentation Project Synopsis & Presentation

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Semantic Segmentation Project Thesis Writing

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