Work

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

Year: 2025

Type: article

Abstract: 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.

Source: Electronics

DOI: 10.3390/electronics15010128

Authors: Yangzhuo Chen, Yuhang Wu, Xiaoliang Wu, Weiwei He, Guangtian He, Xiaowen Cai

Institution: Xiangtan University

Language: English

Topic: Industrial Vision Systems and Defect Detection

Subfield: Industrial and Manufacturing Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,896