Object Detection Final Year Projects with Source Code

Byadmin Final Year Projects

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

Object Detection 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. Abnormality Detection in Chest X-Ray via Residual-Saliency From Normal Generation
    This project develops a smart system to detect diseases in chest X-rays. It works by creating a “normal” version of a given X-ray and then highlighting differences between the original and normal images. These differences point to possible diseased areas. The system uses these highlighted areas to improve detection accuracy and is tested on large public X-ray datasets.
  3. DeepSkin: A Deep Learning Approach for Skin Cancer Classification
    This project focuses on detecting skin cancer early using computer-based methods. It uses images of different skin lesions from a large dataset to teach a computer to recognize cancer types. Advanced deep learning models, like CNNs with DenseNet169 and ResNet50, are applied to improve accuracy. The goal is to help doctors identify skin cancer more reliably and quickly.
  4. Exudate Regeneration for Automated Exudate Detection in Retinal Fundus Images
    This project focuses on detecting early signs of diabetic eye disease from retinal images. It creates a method to generate and highlight disease spots using a small set of open-source images. A custom neural network is developed to classify these spots accurately. The system performs very well, achieving perfect results on the test data.
  5. Bone Stick Image Classification Study Based on C3CA Attention Mechanism Enhanced Deep Cascade Network
    This project focuses on classifying ancient bone sticks unearthed in China using artificial intelligence. It develops a deep learning model that can accurately identify fracture locations and colors on the bone sticks. The model uses advanced attention techniques to focus on important features and reduce background interference. As a result, it achieves high accuracy, making the classification of these historical artifacts much faster and more reliable.
  6. Learning From Multiple Expert Annotators for Enhancing Anomaly Detection in Medical Image Analysis
    This project focuses on improving computer programs that detect problems in medical images, like X-rays. Normally, these programs learn from labels given by doctors, but different doctors may disagree, which lowers accuracy. The researchers developed a method to combine labels from multiple doctors and figure out the most likely “true” label. This makes the program more reliable and accurate at spotting abnormalities in medical images.
  7. Lung-RetinaNet Lung Cancer Detection Using a RetinaNet With Multi-Scale Feature Fusion and Context Module
    This project focuses on developing an automated system to detect lung tumors quickly and accurately. It uses a deep learning model called Lung-RetinaNet, which combines features from multiple layers to improve tumor detection, especially for small tumors. The system achieves very high accuracy and outperforms existing methods, making early diagnosis faster and more reliable.
  8. 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.
  9. Multi-Camera 3D Object Detection for Autonomous Driving Using Deep Learning and Self-Attention Mechanism
    This project focuses on detecting 3D shapes of vehicles using only regular cameras, without needing special depth sensors. It uses multiple cameras installed on the road to gather information and predicts the vehicle's position and orientation. A vision transformer improves accuracy, especially in difficult or blocked views. Finally, it combines data from all cameras to choose the most reliable 3D detection.
  10. Research on Asparagus Recognition Based on Deep Learning
    This project focuses on making asparagus farming faster and more efficient. It uses a computer program to quickly detect asparagus plants for mechanized harvesting. The program is accurate and works well even with interference. This approach helps reduce labor costs and supports modern, large-scale farming.
  11. An AI Based Automatic Translator for Ancient Hieroglyphic LanguageFrom Scanned Images to English Text
    This project builds an AI system that can read ancient Egyptian hieroglyphs and translate them into English. It first identifies the symbols in scanned images and then converts them into clear English text. The goal is to help visitors understand historical sites more easily. The system uses different AI methods and shows strong results in both recognition and translation.
  12. EC2Net Efficient Attention-Based Cross-Context Network for Near Real-Time Salient Object Detection
    This project focuses on detecting important objects in images quickly and accurately. The researchers designed a new method called EC2Net, which combines simple and deep image features efficiently. Their approach uses special modules to preserve object boundaries and improve understanding of complex patterns. Tests showed it works well while using less memory and computing power than existing methods.
  13. Propounding First Artificial Intelligence Approach for Predicting Robbery Behavior Potential in an Indoor Security Camera
    This project develops an AI system to predict and detect potential robberies using indoor surveillance cameras. It uses three detection modules to identify head covers, crowds, and loitering behavior. The system combines advanced object detection and tracking with expert rules to decide robbery risk. Tests on real surveillance videos show it can detect robberies more accurately, helping operators prevent incidents and manage multiple cameras effectively.
  14. Travel Direction Recommendation Model Based on Photos of User Social Network Profile
    This project creates a smart travel recommendation system using photos from a user’s social media account. It analyzes images and related data to suggest countries the user might like to visit. The system uses machine learning methods to compare, classify, and group data for accurate suggestions. Tests show it can correctly predict trips most of the time, and it can improve further by using photo location information.
  15. Uncovering Archaeological Sites in Airborne LiDAR Data With Data-Centric Artificial Intelligence
    This project uses drones and AI to help archaeologists find burial mounds more efficiently. It turns 3D LiDAR data into 2D images and trains an AI model to identify likely sites. A special technique reduces mistakes by checking if the shapes match real mounds. This makes it easier for archaeologists to focus on promising locations in the field.
  16. DeepSkin A Deep Learning Approach for Skin Cancer Classification
    This project focuses on detecting skin cancer early using artificial intelligence. It uses a large dataset of skin images to teach a computer to recognize different types of skin lesions. The system processes images to remove noise and improve quality before training deep learning models. Advanced neural networks like DenseNet169 and ResNet50 are used to accurately classify the skin lesions.
  17. Multi-Exposure Fusion With Guidance Information Night Color Image Enhancement for Roadside Units
    This project improves traffic surveillance images taken at night. It combines camera and radar data to make moving vehicles and objects clearer. The method enhances colors and details while avoiding overexposure and ghosting. Overall, it produces high-quality images that are easier to analyze for traffic monitoring.
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Object Detection Project Synopsis & Presentation

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Object Detection Project Thesis Writing

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