# PRC-Light YOLO: An Efficient Lightweight Model for Fabric Defect Detection
## 7 Conclusions  
In response to the complex nature of defect types in fabrics, which vary in size, the computational intensity of detection models and the lower detection accuracy, this paper proposes the defect detection model PRC-Light YOLO. PRC-Light YOLO achieves network lightweight by replacing certain Conv layers with PConv layers in the Backbone. During the feature fusion stage, the RFB pyramid and the CARAFE upsampling operator are employed, and this approach enlarges the neural network's receptive field and enhances detection accuracy without significantly increasing the model's parameter count or computational load. PRC-Light YOLO adopts the HardSwish activation function, which exhibits piecewise characteristics that reduce computational costs and minimize memory access. Additionally, it utilizes Wise-IOU v3 as the bounding box loss function during model training, demonstrating robust performance in scenarios with varying dataset scales and highly imbalanced positive-to-negative sample ratios. Finally, we design a fabric defect detection system.  
In order to improve the model's robustness and generalization ability, we extended the fabric dataset by applying diverse augmentation techniques, including random rotation, noise addition and adjustments to brightness, chromaticity and saturation. Ablation experimental results reveal that PRC-Light YOLO has a lower reduction in network model parameters by 18.03% and computations by 20.53% when compared with YOLOv7. Additionally, it showcases a 7.6% increase in \(mAP\), enhancing the performance of detecting defects in fabrics. Furthermore, comparative experiments with other object detection models show that PRC-Light YOLO surpasses them in precision, recall, _F1-score_ and \(mAP\) values. Although CenterNet outperforms PRC-Light YOLO by 6.4 percentage points in detecting Warp hanged, PRC-Light YOLO significantly outshines other models in detecting Yard defects, Stains and Holes with \(AP\) values consistently exceeding 80%. Notably impressive is the \(AP\) value of 94.1% achieved for detecting Holes.  
To sum up, we propose the PRC-Light YOLO model, analyze the results of seven ablation experiments and seven comparative experiments, verify our model's excellent defect detection capability in fabrics and design a fabric defect detection system based on PRC-Light YOLO.  
**Author Contributions:** Conceptualization, B.L. and H.W.; methodology, B.L. and H.W.; software, Z.C.; validation, H.W., Z.C. and Y.W.; investigation, H.W., L.T. and J.Y.; writing--original draft preparation, H.W.; writing--review and editing, H.W. and B.L.; supervision, B.L. and K.Z.; project administration and funding acquisition, B.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Natural Science Foundation of China, grant number 61971339.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data used to support the findings of this study are available from the corresponding author upon request.  
**Conflicts of Interest:** The authors declare no conflicts of interest.