Coffee Bean Defects Automatic Classification Realtime Application Adopting Deep Learning

5,500.00
Aim: The aim of this project is to propose an efficient, real-time system for automatic classification of coffee bean defects using the YOLOv8 deep learning model.

Deep Learning Technique to detect Brain tumor disease using YOLOv8

5,500.00
Aim: The aim of this research is to develop a more effective and efficient brain tumor segmentation system using the YOLOv8 architecture.

Lightweight Detection Algorithm for Breast-Mass Features in Ultrasound Images

5,500.00

Aim:

Ā  Ā  Ā  Ā  The project aims to design a lightweight, high-precision breast-mass detection framework using YOLOv11 that can accurately identify lesions in ultrasound images. It seeks to reduce false detections and enable real-time performance on medical imaging systems.

LMD_YOLO: A Lightweight and Efficient Model for Pavement Defects Detection

5,500.00

Aim:

Ā  Ā  Ā  Ā  Ā To develop a lightweight, accurate, and efficient YOLO-based deep learning model for detecting and classifying pavement defects such as cracks and potholes in real time, optimized for deployment.

Lung Nodule Detection in Medical Images Based on Improved YOLOv5

5,500.00
Aim: To enhance the YOLOv8 model for achieving high-performance object detection in medical imaging and other specialized applications.