Defect detection in industrial components based on endoscopic images is a prevalent application in the industrial field. In practical scenarios, the images captured by endoscopes often exhibit characteristics such as low light and poor resolution, which lead to issues like low accuracy and high complexity in detection algorithms, posing challenges to existing industrial component inspection networks. To address these challenges, this paper proposes an Industrial Endoscope Defect Detection Network (IEDD-Net) based on YOLOv12n. First, a Dynamic ConvFormer-GLU Module (DCFG) is designed to enhance the detection capability for micro and small defects. Second, a pinwheel convolutional sampling layer (PCSL) is proposed, which enhances the extraction of defect features. In addition, a new detection head is redesigned to reduce the computational cost and model parameters while maintaining detection accuracy. The results demonstrate that, compared to the original model, IEDD-Net achieves improvements of 17.4%, 17.2%, and 7% in Precision (P), Recall (R), and mAP50, respectively, on an aviation engine endoscopic defect image dataset, while reducing parameters and computational cost. This confirms the efficacy of the propose improvements.