Reducing the latency and size of a deep CNN model for surface defect detection in manufacturing

Abstract

This paper presents the results of applying optimization techniques, most notably neural architecture search (NAS) and hyperparameter optimization (HPO) strategies, to a known state-of-the-art deep learning model for surface defect detection in industry. It will be shown that it is possible to achieve a significant reduction in model latency and its number of parameters, while incurring only a negligible drop in accuracy. The main motivation for this was deployment of surface defect detection models on edge devices with very limited computational capabilities, e.g. a Raspberry Pi. Such deployment requirements are becoming more and more ubiquitous, as it is very expensive to install and maintain many high-end machines in industrial environments.