The field of automated defect detection and quality control is rapidly advancing, with a focus on developing innovative methods and systems that can accurately identify defects and ensure the quality of products. Recent research has explored the use of deep learning models, such as YOLO and U-Net, for defect detection in various industries, including manufacturing, healthcare, and automotive. These models have shown promising results in detecting defects and improving quality control. Additionally, researchers have investigated the use of multi-camera systems, domain adaptation, and feature disentanglement to improve the accuracy and robustness of defect detection systems. Noteworthy papers include: Unsupervised Defect Detection for Surgical Instruments, which proposes a versatile method for detecting defects in surgical instruments, and Multi-View Camera System for Variant-Aware Autonomous Vehicle Inspection and Defect Detection, which presents an end-to-end perception system for variant-aware quality control in real-time. These advancements have the potential to significantly improve the efficiency and accuracy of quality control processes, reducing the risk of defects and improving product safety.