The characteristic size and surface quality of O-ring seals (hereinafter referred to as “O-ring”) used in aerospace and guided weapon systems are important factors affecting the reliability of the main engine, and must be 100% fully checked. Due to the flexible characteristics of the O-ring material and the omnidirectional curved surface feature of the outer surface, the current manual measurement and detection methods have three major drawbacks: low efficiency, unstable results and high manpower consumption, which can no longer meet the requirements of the rapid development of aerospace and defense industries. With the advent of convolutional neural networks, target detection algorithms based on deep learning are widely used in the field of target detection because of their simple structure and good versatility. The micro-aerospace the O-ring studied in this paper has an inner diameter size range of Φ1.8 mm-Φ20 mm. Through the analysis of the surface topography of the defects, it is found that most of the defects have the characteristics of tiny targets and the pixels of the marked defects are less than 0.33% of the total pixels of the image, which is a typical tiny target detection. Compared with other computer vision tasks, tiny target detection has problems like fewer available features, higher positioning accuracy requirements, lower proportions of tiny targets in datasets, sample imbalance and tiny target aggregation. Because of its omnidirectional curved surface features, the O-ring presents severe bright areas and dark areas in images from any angle. The random defects are intertwined with these non-uniform areas which causes great difficulties in the detection and classification of surface defects. Especially for micro-O-ring, tiny defects impose higher demands on algorithm sensitivity and classification ability. Although the target detection algorithm based on deep learning has good detection capability but its detection efficiency is relatively low, and there is room for improvement in detection accuracy.To address the above problems, two deep-learning-based algorithms are proposed for detecting surface defects on the O-ring. By adding multi-head attention mechanisms to the inverse residual blocks of MobileNetv2, we constructed a lightweight backbone network called Efficient Model. By using the Next Hybrid strategy, we fused multiple attention mechanism modules from the industrial-grade Transformer network to build a Next Generation Vision Transformer backbone network. In each of these two backbone networks, feature extraction networks were added to design the Efficient-FPN Model and Transformer-FPN Model detection algorithms. The experimental results show that the mAP of the Efficient-FPN Model and Transformer-FPN Model detection algorithms is higher than that of YOLOv5s, YOLOv5x and YOLOv5z, among which the mAP of the Transformer-FPN model is the highest, reaching 91.4%. The Efficient-FPN Model has the fastest detection speed of the five models, reaching 110.8 frame/s. The mAP of the Efficient-FPN Model reached 86.1%, which was also higher than other YOLOv5 algorithms, and it was the detection model with the best comprehensive performance. The above algorithm is deployed in the self-developed intelligent measurement and inspection equipment of aerospace seal ring, and the purpose of detecting omnidirectional curved flexible parts is realized quickly and accurately.