Back to archive
Computer Vision
2025
Individual coursework

Swimming Pool Detection

Computer vision coursework project focused on transfer learning and aerial object detection.

Built a YOLOv11 object detection model to identify swimming pools from aerial images. Achieved 95.5% mAP after 30 epochs using transfer learning on a custom-labeled dataset with GroundingDINO + Roboflow.

Key Outcomes

95.5% mAP after 30 epochs
Transfer learning with custom aerial labels
GroundingDINO and Roboflow data workflow

Context

Problem and Context

Built a YOLOv11 object detection model to identify swimming pools from aerial images. Achieved 95.5% mAP after 30 epochs using transfer learning on a custom-labeled dataset with GroundingDINO + Roboflow.

Approach

Approach and Architecture

YOLOv11 aerial detection of pools using transfer learning and custom labels.

Implementation

Implementation Details

YOLOv11
GroundingDINO
Roboflow
Object Detection
Python
OpenCV

Results

Results and Tradeoffs

This project is presented as a concise technical overview rather than a full-length narrative case study.

95.5% mAP after 30 epochs
Transfer learning with custom aerial labels
GroundingDINO and Roboflow data workflow

Explore More

Related Projects

Browse adjacent work from the same archive group or jump back to the project archive.

Back to archive