Light-weight optical fiber adapter rotating target detection method based on improved YOLOv8n

By improving the YOLOv8n model, combined with modules such as MobileViTv3, Ghost Bottleneck v2 and Dynamic Head, the loss function and label allocation strategy are optimized, and the problem that traditional object detection methods are difficult to achieve high precision and real-time in complex fiber patch panel scenarios is solved, and efficient rotation frame object detection and lightweight deployment are achieved.

CN119810425AInactive Publication Date: 2025-04-11BEIJING UNIV OF POSTS & TELECOMM
9 Cites 3 Cited by

Patent Information

Application Number
CN202510046212.1
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-01-13
Publication Date
2025-04-11
Estimated Expiration
Not applicable · inactive patent

AI Technical Summary

Technical Problem

Traditional object detection methods are difficult to achieve the dual requirements of high accuracy and real-time in complex scenarios such as fiber patch panels, especially when dealing with fiber adapters in arbitrarily oriented.

Method used

Based on the improved YOLOv8n's lightweight fiber adapter rotation object detection method, the Ghost Bottleneck v2 module is introduced to the neck network by integrating the MobileViTv3 module into the C2f module of the backbone network, the Ghost Bottleneck v2 module is introduced to the neck network, the Dynamic Head is used to improve the detection head structure, and the Angle DFL Loss and Dynamic ATSS is introduced to optimize the loss function and label allocation strategy.

Benefits of technology

The requirement for high-precision detection and lightweight deployment in fiber adapter detection tasks is realized, significantly reducing computing overhead and improving feature extraction and fusion efficiency, suitable for deployment on embedded devices.

✦ Generated by Eureka AI based on patent content.
Patent Text Reader

Abstract

The invention provides a light-weight optical fiber adapter rotating target detection method based on improved YOLOv8n, and relates to the technical field of computer vision and target detection, and the method comprises the following steps: collecting an optical fiber adapter image in a real scene, marking data, and carrying out the data enhancement to expand a data set; the method comprises the following steps: carrying out improvement on the basis of a YOLOv8n OBB model, and integrating MobileViTv3 into a C2f module of a backbone network; the Ghost Bottleneck v2 is integrated to a C2f module of the neck network; the structure of the detection head is improved by using Dynamic Head; the improved YOLOv8n-OBB lightweight model is used for training evaluation, and Angle DFL Loss is introduced in the training process to serve as a loss function; a label distribution strategy DynamicATSS module is integrated; completing the training, and keeping the optimal weight model after the training for experimental testing; according to the method, the detection requirements of high precision and low resources of an optical fiber adapter identification model are fully considered, the detection precision and the lightweight degree of the model can be effectively improved, and the method is suitable for efficient operation in embedded equipment and can be effectively used for detection of the optical fiber adapter.
Need to check novelty before this filing date? Find Prior Art