Open AccessPDDD-Net: Defect Detection Network based on Parallel Attention Mechanism and Dual-Channel Spatial Pyramid Pooling
Author(s) -
Tingting Sui,
Junwen Wang
Publication year - 2023
Publication title -
ieee access
Language(s) - English
Resource type - Journals
ISSN - 2169-3536
DOI - 10.1109/access.2023.3343566
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
Owing to the small size of the defect pixel area and poor defect-background contrast issues in industrial images, noise and missed detection can easily occur. Therefore, automated defect detection is both necessary and challenging. To address these issues, with parallel attention mechanism (PAM) and dual-channel spatial pyramid pooling-fast block (DC_SPPF), a novel defect detection network, namely, PDDD-Net, is proposed in this paper. First, to make the detection network emphasize small defect areas better, the PAM block is proposed to be embedded into YOLOv5 to obtain more low-level visual features and improve the detection accuracy of microdefects. Meanwhile, by fusing multi-channel features, the DC_SPPF block is proposed to replace the raw spatial pyramid pooling-fast block to acquire richer discriminative features of the defect areas. Finally, The soft non-maximum suppression (Soft-NMS) module is used to undertake the feature candidate box filtering task in YOLOv5 to reduce missed detection. Two public datasets are adopted for the model evaluation: the Tianchi aluminum profile defects dataset (APDDD) and the power line insulator dataset (CPLID). The experimental results indicate that the proposed PDDD-Net network exhibits remarkable defect detection performance compared with other related detection methods.