Object detection survey for industrial applications with focus on quality control

Abstract The industrial quality control plays a key role in ensuring flawless products and efficient production processes. In sectors such as automotive, electronics, and packaging, manufacturers face increasing pressure to detect defects early, minimize scrap, and meet strict regulatory and customer requirements. Traditional manual inspection methods such as visual checks and manual measurements are often slow, inconsistent, and prone to human error. In the past few years, computer vision-based methods, in particular object detection, have established themselves as powerful tools for automating and improving quality inspections. This article presents a survey regarding the present status of research on state-of-the-art object detection methods in an industrial context. It explains technical functionalities, discusses advantages and disadvantages with regard to requirements such as accuracy, speed and robustness and presents specific industrial applications, for example for defect detection and component measurement. The paper concludes with a comparative analysis of the methods, focussing on their suitability for various industrial scenarios. The objective is to provide recommendations for the efficient use of object detection in industrial quality control and to identify potential future research directions.