Optimizing Cost Estimation Using YOLOv9

Accurate wall measurements are essential for esti- mating painting costs, yet traditional methods rely on manual tools such as ladders and tape measures, making the process error-prone and potentially hazardous. This research study proposes an AI-driven approach for automating wall measurement and cost estimation through images captured by a mobile device. Leveraging YOLOv9, a cutting-edge object detection model, the proposed method identifies large surfaces, such as walls, with enhanced speed and accuracy over previous YOLO versions. By calculating wall area directly from detected dimensions, this approach translates measurements into cost estimates using standardized painting rates. This automated system not only minimizes manual effort but also improves measurement accu- racy and safety by eliminating physical tools. Real-world testing demonstrates the model's effectiveness in providing accurate wall detection and cost prediction, offering a practical solution for both homeowners and painting professionals aiming to streamline estimation and planning processes.