# PRC-Light YOLO: An Efficient Lightweight Model for Fabric Defect Detection
## 4 Experiment
### Comparison Experiment  
Through comparison experiments, the efficacy of various models can be assessed so that the better method can be selected. Therefore, we choose to conduct comparison experiments with Faster R-CNN, SSD, EfficientDet, CenterNet and YOLOv8x target detection models.  
Table 3 shows a performance comparison of defect detection among various object detection models, with \(P\), \(R\), \(F1\)-_score_, \(mAP\) and inference time selected as evaluation metrics.  
It can be seen that the two-stage detection model Faster R-CNN has the lowest \(mAP\) value compared to the other detection algorithms; in addition, the two-stage detection model has one more step of operation compared to the single-stage model: the candidate  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Categories** & **Faster R-CNN** & **SSD** & **EfficientDet** & **CenterNet** & **YOLOv7** & **YOLOv8x** & **PRC-Light YOLO** \\ \hline Warp hanged & 0.738 & 0.756 & 0.731 & **0.802** & 0.636 & 0.732 & 0.738 \\ Yard defects & 0.582 & 0.596 & 0.599 & 0.652 & 0.77 & 0.768 & **0.839** \\ Stain & 0.692 & 0.759 & 0.686 & 0.765 & 0.779 & 0.786 & **0.803** \\ Hotel & 0.773 & 0.814 & 0.773 & 0.804 & 0.832 & 0.850 & **0.941** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Detection effects of different models.  
Figure 12: Detection effect of the PRC-Light YOLO.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Models** & \(P\) & \(R\) & \(F1\) & **Params/M** & **GLOPs/G** & \(mAP\%\)** & **Inference Time/ms** \\ \hline Faster R-CNN & 0.784 & 0.491 & 0.6 & 136.98 & 370.3 & 0.696 & 177.35 \\ SSD & 0.832 & 0.529 & 0.647 & 105.2 & 87.41 & 0.731 & 143.58 \\ EfficientDet & 0.787 & 0.492 & 0.605 & 52.11 & 34.97 & 0.697 & 22.71 \\ CenterNet & 0.838 & 0.547 & 0.662 & 125 & 69.66 & 0.756 & 158.2 \\ YOLOv7 & 0.826 & 0.697 & 0.76 & 37.21 & 105.2 & 0.754 & 32.08 \\ YOLOv8x & 0.754 & 0.754 & 0.754 & 68.23 & 258.5 & 0.784 & 40.39 \\ PRC-Light YOLO & **0.859** & **0.784** & **0.82** & **30.50** & **83.6** & **0.83** & **28.46** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Defect detectability of different models.  
regions are first generated, and then the regions are classified and identified, which also causes the slower training speed and inference time of Faster R-CNN. Compared with the target detection models of Faster R-CNN, SSD, EfficientDet, EfficientDet, YOLOv7 and YOLOv8x, the precision of PRC-Light YOLO is improved by 7.5%, 2.7%, 7.2%, 2.1%, 3.3% and 10.5%. The recall is increased by 29.3%, 25.5%, 29.2%, 23.7%, 8.7%, 3%; the _F1-score_ is increased by 20%, 17.3%, 21.5%, 15.8%, 6%, 6.6%, and params is decreased by 77.7%, 71%, 41.5%, 75.6%, 18%, 55.3%. In terms of _GFLOPs_, PRC-Light YOLO is lower than Faster R-CNN, SSD, YOLOv7 and YOLOv8x but higher than EfficientDet and CenterNet, whereas in terms of _mAP_, PRC-Light YOLO outperforms EfficientDet and CenterNet by 13.3% and 7.4%. In terms of inference time, EfficientDet is faster than PRC-Light YOLO, but it is lower than PRC-Light YOLO in terms of P, R, F1 as well as _mAP_ values, and PRC-Light YOLO is faster than all other models except EfficientDet. Comparing with other models, it can be found that PRC-Light YOLO is effective at detecting the Hole, and the _AP_ value reaches 94.1%. In the case of Warp hanged, the detection effect of the PRC-Light YOLO model is not satisfactory, and the _AP_ value is only higher than that of EfficientDet, YOLOv7 and YOLOv8x. Therefore, in the subsequent experiments, the PRC-Light YOLO model should be optimized by taking into full consideration the characteristics of the Warp hanged. By comparing the _AP_ values of each model for detecting yard defects and stains, PRC-Light YOLO has at least 25.7% and 11.7% higher detection accuracy than the other models. Comprehensive indications reveal that our proposed PRC-Light YOLO has an outstanding detection performance on the fabric dataset, which validates the effectiveness of the improved method.