## 1 Introduction
It is based on the analysis of the deviation from the mean line profile that is defined as an average of the pixel brightness of five vertical lines in the image of a powder bed [19]. The algorithm was further developed by [PERSON] et al. [20] to identify a greater spectrum of defects. However, this method does not provide coverage of the entire powder platform. [PERSON] et al. [21] identified recoater hopping as the most common and challenging defect to detect. Recoater hopping occurs when the recoater blade either slightly strikes the already sintered part just beneath the powder layer during its movement or vibrates during operation [22]. To accurately extract defect features, various local binary patterns (LBP) operators were tested, and the multi-block local binary pattern (MB-LBP) was identified as the most promising [21]. [PERSON] et al. [16] applied the bag of keypoints machine learning algorithm to autonomously detect and classify significant anomalies related to the powder spreading process in an EOS LPBF machine. It allowed the location of regions within the final part that may contain macroscopic (millimeter-scale) flaws [22]. This algorithm was further refined to enhance the precision of defect localization [16]. Despite these advancements, challenges remain in achieving robust and reliable defect recognition systems. Factors such as the variability in powder material properties and environmental conditions can affect the accuracy of defect detection. Moreover, the high volume of data generated during LPBF necessitates efficient data processing and analysis techniques to enable real-time decision-making. According to [PERSON] et al. [23], deep learning object detectors are mainly classified into two types: two-stage and single-stage detectors. Two-stage detectors involve a preprocessing step to generate object proposals before classification and bounding box regression. In contrast, single-stage detectors operate end-to-end without this step, making them more computationally efficient and faster, ideal for real-time applications on resource-constrained devices. Two stage object detectors include among others: region-based CNN (R-CNN), fast R-CNN, and faster R-CNN. One-stage detectors include single shot detector (SSD), deconvolutional single shot detector (DSSD), and YOLO [24]. One of the examples of one-stage detectors is You Only Look Once (YOLO), initially introduced by [PERSON] et al. in 2016 [25], which later evolved for better accuracy. It has been successfully used in real-time applications like weed detection in cotton production systems [26] or weak surface defect detection for plastic bottles in a production line [27]. The comparison of RetinaNet, single shot multibox detector (SSD), and YOLO in the recognition of pills at the hospital [28] demonstrated that RetinaNet has a higher mean average precision (MAP) but runs at only a third of YOLO v3's frames per second (FPS), limiting its real-time use. In addition, SSD lags in both MAP and FPS. Similar conclusion were drawn by [PERSON] et al. [29]. YOLO outperformed faster R-CNN with ResNet offering excellent balance of speed and accuracy [29]. Based on this characteristic, the YOLO algorithm is often successfully applied in real-time applications in additive manufacturing [24,30]. However, the algorithm requires time-consuming image annotation; therefore, the large language models (LLMs) are starting to be tested [31]. This research conducts the first comprehensive performance evaluation of state-of-the-art YOLO object detectors for identifying multiple powder bed defect appearing during powder bed fusion. This research is anticipated to advance future studies on machine vision-based systems for monitoring powder beds.