Survey on 3D Anomaly Detection Regarding Defect Size: Tradeoff Between Accuracy and Efficiency With Future Research Direction

Recent advancements in neural networks has enhanced image anomaly detection in different industries such as manufacturing, reduced human intervention, and boosted productivity and quality. However, there are still some challenges in using point cloud and RGB data for anomaly detection across varying defect sizes. This study explores such challenges and and highlights the importance of high-quality data for effective training. It also highlights the trade-off between detection accuracy and inference speed, where faster models tend to sacrifice performance on smaller defects due to the use of general loss functions that do not sufficiently capture fine-grained anomalies. This work suggests that future research should focus on improving the models that efficiently detect small defects while ensuring high-speed inference and bridging the gap between accuracy and efficiency for real-world industrial applications.