Below are some of the reports I have written. You can view or download them as needed.
This report focuses on the integration of advanced object detection and human face tracking technologies with drone systems. It explores how drones equipped with AI can enhance surveillance capabilities in various environments, providing real-time data collection and analysis. The study evaluates various machine learning models for detecting and tracking objects and faces, optimizing drone flight paths, and improving the accuracy and efficiency of surveillance operations.
This report presents an innovative approach to implementing real-time face detection using the DJI Tello drone. The project integrates computer vision algorithms with drone flight controls to autonomously detect and track human faces in dynamic environments. The report details the design and implementation process, including challenges faced, performance analysis, and future improvements for enhancing the drone's autonomous capabilities in face recognition tasks.
This research explores the use of Convolutional Neural Networks (CNNs) for classifying tomato leaf diseases based on image data. The aim is to improve disease detection in tomato crops to mitigate production losses. The paper highlights the effectiveness of CNNs and discusses key technologies like Transfer Learning and pretrained models (AlexNet, VGG16, ResNet, DenseNet) for identifying tomato leaf diseases.
This report details the design and development of a Smart Trolley system that aims to simplify and improve the shopping process. The system integrates both hardware and software components, utilizing a Raspberry Pi as the core controller. Key features of the smart trolley include automated item detection via a barcode scanner, a touchscreen interface for user interaction, and real-time billing through a mobile payment system (SmartPay). The system is designed to optimize the customer shopping experience, providing ease of use and streamlining the checkout process.