The Story

The client needed a machine learning model capable of identifying PCBs within a dataset despite challenging variations. The project aimed to ensure reliable performance for real-world applications while addressing image variability. Real-time or near-real-time predictions were essential to meet their operational demands.

Our client is a technology-focused company aiming to enhance their PCB identification process using machine learning. They sought a scalable, efficient solution to handle variations in image conditions and ensure real-time predictions.

Abhay Mathur

Head of Mobile Development

Challenges

• > Handling variability in PCB images due to angles, scales, and environmental conditions.

• > Achieving high accuracy for practical applications.

• > Optimizing computation time for real-time or near-real-time predictions.

The Solution

The solution incorporated transfer learning using the VGG 16 CNN architecture for feature extraction, leveraging pre-trained weights for accuracy and efficiency. Implemented in Python with Keras, the model used Kubeflow on Google Cloud Platform for orchestration and deployment. Scikit-learn supported data pre-processing and evaluation, while OpenCV handled image manipulation. A user-friendly interface was developed using Flask, ensuring seamless interaction.

The Outcome

The project achieved a remarkable accuracy of over 85%, effectively identifying PCBs across variations in angle, scale, and conditions. Predictions were optimized to less than 2 seconds, meeting the requirement for real-time performance. The project exemplified the power of transfer learning and a modern tech stack in addressing complex image recognition challenges.