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The advent of Additive manufacturing (AM) of 3D printed objects is revolutionising the manufacturing industry. Despite its promise, the extensive post-processing requirements of 3D objects remains a significant barrier to AM’s wider adoption. Identifying defects in real-time from powder bed images taken before and after the laser melting of the current layer presents a promising strategy to reduce post-processing efforts. Traditional methods focusing on the top-layer images fall short in identifying multi-layer defects such as keyhole porosity, balling, and lack of fusion, which are critical to the integrity of 3D printed objects. Addressing this challenge, our study introduces an innovative multi-layer technique for the detection of keyhole porosity using high-quality X-ray Computed Tomography (XCT) images, leveraging the capabilities of the cutting-edge YOLO (You Only Look Once) object detection algorithm. Our findings reveal that this approach achieves a remarkable mean average precision (mAP) score of 92.585%, underscoring the efficacy of deep learning models in accurately identifying keyhole porosity across XCT images. This research not only demonstrates the potential for improving the quality and reliability of AM processes but also paves the way for reducing the dependency on labour-intensive post-processing steps.