Machine Learning
Smart Hole Detector
An ML-powered system that autonomously detects body holes on vehicles — optimizing sticker placement to minimize road noise and prevent water damage.
Overview
In collaboration with a talented team, I developed a groundbreaking Smart Hole Detector system. This project aimed to automate the detection of body holes on vehicles, optimizing sticker placement to minimize road noise and prevent water damage. The development process spanned over two days and showcased our expertise in computer vision and machine learning.
Features
- Combination of image processing and deep learning techniques for accurate detection and classification of body holes — in real-time or via image processing
- Integrated cutting-edge technologies including Python, OpenCV, and the YOLOv8 object detection training system
- Fully autonomous and efficient hole detection — reducing manual efforts and enhancing overall productivity
- Systematic engineering approach to training, validation, and deployment of the ML model
Why It Matters
Improperly sealed vehicle body holes lead to road noise, water intrusion, and long-term corrosion. By automating detection, this system eliminates human error and dramatically speeds up the assembly line quality check process — turning a manual, error-prone inspection into a reliable, autonomous one.