Aiming at the problem of low efficiency fault detection in industrial production, this paper proposes a fault detection algorithm based on “Faster Objects, More Objects” (FOMO). This method is suitable for Tiny Machine Learning (TinyML) equipment. It can efficiently extract and classify the defect features in industrial products through Convolutional Neural Networks (CNN). In terms of algorithm application, this paper compares the performance differences of (You Only Look Once version 5 (YOLOv5), Single Shot Multibox Detector (SSD) and FOMO in 3D printing defect detection. In the experimental environment built in this paper, FOMO detection algorithm has significant advantages in lightweight, detection speed, recognition accuracy and other aspects compared with other detection algorithms, and its detection accuracy and stability meet the requirements of practical application.