An Improved Single-Stage Object Detection Model and Its Application to Oil Seal Defect Detection

Oil seals, as core industrial components, often exhibit defects with sparse features and low contrast, posing significant challenges for traditional vision-based inspection methods. Although deep learning facilitates automatic feature extraction for defect detection, many instance segmentation models are computationally expensive, hindering their deployment in real-time edge applications. In this paper, we present an efficient oil seal defect detection model based on an enhanced YOLOv11n architecture (YOLOv11n_CDK). The proposed approach introduces several dynamic convolution variants and integrates the Kolmogorov–Arnold Network (KAN) into the backbone. A newly designed parallel module, the nested asynchronous pooling convolutional module (NAPConv), is also incorporated to form a lightweight yet powerful feature extraction network. Experimental results demonstrate that, compared to the baseline YOLOv11n, our model reduces computational cost by 4.76% and increases [email protected] by 2.14%. When deployed on a Jetson Nano embedded device, the model achieves an average processing time of 6.3 ms per image, corresponding to a frame rate of 105–110 FPS. These outcomes highlight the model’s strong potential for high-performance, real-time industrial deployment, effectively balancing detection accuracy with low computational complexity.