AnomalySeg: Deep Learning-Based Fast Anomaly Segmentation Approach for Surface Defect Detection

Abstract

Product quality inspection is a crucial element of industrial manufacturing, yet flaws such as blemishes and stains frequently emerge after the product is completed. Most research has utilized detection models and avoided segmenting networks due to the unequal distribution of faulty information. To overcome this challenge, this work presents a rapid segmentation-based technique for surface defect detection. The proposed model is based on a modified U-Net, which introduces a hybrid residual module (SAFM), combining an improved spatial attention mechanism and a feedforward neural network in place of the remaining downsampling layers, except for the first layer of downsampling in the encoder, and applies this residual module to the decoder structure. Dilated convolutions are also incorporated in the decoder to obtain more spatial information about the feature defects and to reduce the gradient vanishing problem of the model. An improved hybrid loss function with Dice and focal loss is introduced to alleviate the small defect segmentation problem. Comparative experiments were conducted on different segmentation-based inspection methods, revealing that the Dice coefficient (DSC) evaluated by the proposed approach is better than previous generic segmentation benchmarks on KolektorSDD, KolektorSDD2, and RSDD datasets, with fewer parameters and FLOPs. Additionally, the detection network displays higher precision in recognizing the characteristics of minor flaws. This paper proposes a practical and effective technique for anomaly segmentation in surface defect identification, delivering considerable improvements over previous methods.

A Real-Time Dual-Task Defect Segmentation Network for Grinding Wheels with Coordinate Attentioned-ASP and Masked Autoencoder

“…Moreover, the combination of ASPP and the Convolutional Block Attention Module (CBAM) [18] or Coordinate Attention (CA) [19] is frequently adopted to solve respective tasks [20][21][22][23][24][25][26]. Similar approaches can be found in other architectures like the U-Net [27][28][29][30][31]. These studies have achieved state-of-the-art experimental results in their respective tasks.…”

“…Incorporated after each layer of the encoder to enhance its feature extraction capability [27,30,[32][33][34];…”

Research on Mobile Phone Backplane Defect Segmentation Based on MDAF-UNet

“…In recent years, researchers have begun to explore the use of CNN-based network models for defect segmentation to improve segmentation accuracy [16][17][18]. Song et al [19] developed a U-Net-based surface defect detection technique for identifying surface flaws commonly found in industrial production products, offering robust support for enhancing the quality of industrial production. Jiang et al [20] further applied UNet to mobile phone backplane defect detection and achieved better results.…”