Multi-defect detection and classification for aluminum alloys with enhanced YOLOv8.

ABSTRACT: With the increasing application of aluminum alloys in the industrial field, the defect of aluminum alloys significantly impacts the structural integrity and safety of products. However, state-of-the-art material defect detection methods have low detection accuracy and inaccurate defect target frame problems. Therefore, an enhanced YOLOv8-ALGP (aluminum, Ghost, P2) defect detection and classification method for 13 defects is proposed in this paper. Firstly, based on the AliCloud Tianchi dataset, 3 defects are added and an enhancement strategy is implemented to increase the diversity of the training dataset, which improves the generalization ability of the model. Secondly, an ALGC3 (aluminum, Ghost, Concentrated-Comprehensive Convolution Block (C3)) module is introduced to optimize the fusio

SUBMITTER: Han Y 

PROVIDER: S-EPMC11925294 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature