## 1 Introduction  
Industrial production and maintenance are under constant pressure from increasing quality requirements due to rising product demands, changing resources, and cost specifications. In addition, there are constantly changing framework conditions due to new and changing legal requirements, standards, and norms. Ultimately, the increasing general flow of information via social media and other platforms leads to an increasedrisk of reputational damage from substandard products. These influences, combined with the fact that quality assurance is still predominantly performed or supported by human inspectors, have led to the need for advances in continuous quality control. Since vision is the predominant conscious sense of humans, most inspection techniques in the past and even today are of a visual nature [1]. However, manual visual inspection (VI) has several drawbacks, which have been studied, for example, by [PERSON] et al., [PERSON] et al., and [PERSON] et al. [2; 3; 4], specifically including high labor costs, low efficiency, and low real-time performance in the case of fast-moving inspection objects or large surface areas. According to [PERSON] and [PERSON] [5], minimal error rates of \(10^{-3}\) can be reached for very simple accept/reject tasks. Though highly dependent on the inspection task, [PERSON] and [PERSON] [6] observed error rates of 20% to 30% in more complex VI tasks in their studies. In addition, decreasing efficiency and accuracy occur during human inspection due to fatigue and resulting attention deficits. As a way to counteract these effects, automation solutions were pushed in the 1980s. The goal was to increase efficiency and performance and reduce costs while minimizing human error. Accordingly, computer vision (CV) methods were introduced to VI, which was initially only relevant for the automation of simple, monotonous tasks. In the beginning, they served more as a support for inspectors [7], but as development progressed, whole tasks were solved without human involvement. This was the beginning of automated visual inspection (AVI). With deep learning (DL) becoming the technology of choice in CV since 2010, showing better generalization and less sensitivity to application conditions than traditional CV methods, new models have reached performance levels that even surpass those of humans in complex tasks like image classification, object detection, or segmentation [8; 9; 10]. Accordingly, the development and use of such DL techniques for use in industrial AVI are reasonable and understandable. In order to provide an overview of the current state of research and development and to understand what the current focus is, we provide answers to the following guiding questions in our study. 1. What are the requirements that have to be considered when applying DL-based models to AVI? 2. Which AVI use cases are currently being addressed by deep-learning models? 3. Are there certain recurring AVI tasks that these use cases can be categorized into? 4. What is the data basis for industrial AVI, and are there common benchmark datasets? 5. How do DL models perform in these tasks, and which of them can be recommended for certain AVI use cases? 6. Are recent state-of-the-art (SOTA) CV DL models used in AVI applications, and if not, is there untapped potential? From these questions, we derive key insights and challenges for AVI and give an outlook on future research directions that we believe will have a positive impact on the field in industry as well as research. From these questions, we further derive the structure for our study as follows. In Section 2, we describe our literature research process and the constraints we defined. This is followed by Section 3, where we derive requirements for DL-based AVI and categorize the surveyed literature based on the use case as well as on how these use cases are solved to answer questions one to three.