Transferable Learning Architecture for Scalable Visual Quality Inspection

Abstract

In recent years, convolutional neural networks (CNNs) have become a de facto standard in computer vision for object detection and recognition. At present, CNNs have been used in many application areas including the automation of industrial manufacturing processes. But using CNN in a real-time environment to track defects on products has many shortcomings like long training time, large data requirements, slow inference time, dynamic environment, and hardware dependency. This paper evaluates the state-of-the-art CNN architectures for object detection to address the mentioned challenges and provide the best possible solution. A set of pre-trained models has been trained on just 781 annotated images by applying transfer learning. Experimental results showed that Faster RCNN with VGG-16 backbone outperforms the other models in case of accuracy and mAP. But RetinaNet with an FPN backbone has the fastest inference time on multi-scaled defects. Paper also presents the deployment pipeline for inference on mobile devices to use in a real-time environment without any special hardware. In addition, an improved dataset of submersible pump impellers, based on the existing Kaggle dataset is introduced.