# A Novel Dataset for Fabric Defect Detection: Bridging Gaps in Anomaly Detection
## 2 Related Work
### Object Detection  
These approaches are often based on one-stage detectors and two-stage detectors, as mentioned. We summarize relevant examples of each in the following subsections.  
**One-stage detectors**. Many works consist in making alterations to YOLO models. [PERSON] et al. used a lightweight CNN model named YOLO-LFD, competitive over other YOLO models, with a much lighter computational load [89]. The same authors later used a new weakly supervised learning framework, named DLSE-Net, to classify fabric defects with 91% accuracy, which, while worse than supervised approaches, outperformed other weakly supervised approaches [90].  
[PERSON] et al. implemented a new Spatial Pyramid Pooling (SPP) module, with Maxpool operations replaced with Softpool, into the YOLOv4 backbone, along with image preprocessing with contrast-limited adaptive histogram equalization (CLAHE), improving over baseline results [91].  
[PERSON] et al. introduced an Atrous Spatial Pyramid Pooling (ASPP) module, along with a convolution squeeze-and-excitation (CSE) attention channel module, into the YOLOv5 backbone [92]. [PERSON] et al. also improved on the YOLOv5 network by replacing the bottleneck structure with a coordinate attention module, switching the SiLU activation function with Mish, the CIoU loss function with SIoU, and combining focal loss and GHM loss functions as the target confidence loss function [93].  
[PERSON] et al. used a modified YOLOv3, with a coordinate attention module and a new tiny defect detection layer, culminating in a new anchor-free detector, YOLOX-CATD, which did not require anchor-related hyperparameter tuning [94].  
**Two-stage detectors**. We found fewer works with approaches based on two-stage detectors. We briefly describe some of the most representative ones.  
[PERSON] et al. improved a faster R-CNN backbone with Gabor filters optimized with genetic algorithms, achieving better accuracy in [63]. [PERSON] et al. used a cascade R-CNN with a switchable atrous convolution layer and an upgraded feature pyramid network [95]. [PERSON] et al. used a network structure based on Faster R-CNN, WALNet, with a dilated convolution module, which employed a multi-scale convolution kernel to adapt to defects of different sizes [96].  
Generative Model-Based Approaches  
Regarding autoencoders, [PERSON] et al. proposed an MXNet-based autoencoder, using cross-patch similarity to detect and reconstruct similarities between different patches of the selected image [97]. [PERSON] et al. used stacked convolutional autoencoders on synthetic datasets, created with a new method, using expert knowledge to extract defect characteristics, with a method that would allow for the creation of new datasets without needing many defect data at all [98]. [PERSON] et al. used a deep denoising convolutional autoencoder (DDCAE), performing image reconstruction with a depth denoising convolution self-encoder, followed by a mathematical morphology analysis of the resulting image [99].  
Regarding GAN-based methods, [PERSON] et al. used an unsupervised method with a deep convolutional GAN that reconstructed a given defect image without the aforementioned defect and compared it to the original image to discover the presence of defects [100]. [PERSON] et al. devised a GAN-based framework capable of automatically adapting to different fabric textures with a customized deep semantic segmentation network [101]. The same author later proposed another approach wherein a GAN model was used to build fault blocks from an acquired distribution of fabric defect features, applying a faster R-CNN for further defect detection.