DMPDD-Net: An Effective Defect Detection Method for Aluminum Profiles Surface Defect

Abstract

Defect detection is an important part of the manufacturing process of industrial products. The processing of aluminum profiles requires more accurate and robust defect detection methods. However, various types of defects, small size of the defect pixel area and high defect-background similarity issues pose challenges to existing industrial defect detection methods for aluminum profiles surface defect (APSD). To address these issues, with dual-path parallel attention mechanism (DP-AM), multifeature fusion mechanism (MFFM) and parallel spatial pyramid pooling fast (PSPPF) module, a novel defect detection network, named DMPDD-Net, is proposed in this article. Specifically, the proposed detection network enhances the feature extraction ability of small-size defects by designing a parallel DP-AM module. Meanwhile, a self-learning factor is set up in the feature fusion formula to construct the MFFM module, which enhances the expression ability of defect features for APSD through multichannel feature fuse mechanism. Additionally, the PSPPF module is proposed to perform spatial pyramid pooling (SPP) in You-Only-Look-Once-Version-Eight model (YOLOv8) to reduce the loss of key features. We validate the effectiveness and improvement of our proposed DMPDD-Net by conducting ablation experiments and algorithm comparisons on the Tianchi aluminum profile surface defect dataset (TAPSDD). Our proposed model outperforms the baseline network with a 3.5% increase in mean average precision (mAP)@0.5 and a 2.8% increase in mAP@0.5:0.95, and a 3.2% increase in formula-one-score (F1) for the TAPSDD dataset. Our research results indicate that our proposed network is a promising alternative to the current defect detection methods for APSD.