# PRC-Light YOLO: An Efficient Lightweight Model for Fabric Defect Detection
## 4 Experiment
### Ablation Experiment  
An ablation experiment is an important method in deep learning; by improving the modules of the YOLOv7 network structure and assessing the contribution of different improvement methods on its performance, we conducted seven sets of ablation experiments in all, where \"\(\check{\check{\vee}}\)\" corresponds to the improvement method and the corresponding experimental order. The experimental results are listed in Table 2. Experiment 1 (exp1) is based on YOLOv7. Experiment 2 (exp2) replaces YOLOv7's SiLU activation function with HardSwish. Experiment 3 (exp3) incorporates the Wise-IOU v3 into YOLOv7 as the bounding box loss function. Experiment 4 (exp4) substitutes YOLOv7's SSPCSP pyramid with the RFB module. Experiment 5 (exp5) replaces the nearest neighbor interpolation method in YOLOv7 with the CARAFE operator. Experiment 6 (exp6) integrates HardSwish, Wise-IOU v3, RFB and CARAFE into YOLOv7. Experiment 7 (exp7) is based on PRC-Light YOLO, and it builds upon exp6 by replacing the \(3\times 3\) convolution operation in the Backbone's E-ELAN with PConv. From Table 2, it can be seen that the first set of experiments is based on YOLOv7, the model parameter number is 37.21 M, the Giga FLoating-Point Operations Per Second (_GFLOPs_) is 105.2 G and the \(mAP\) is 75.4%, followed by subsequent experiments, which are all based on the results of the first set of experiments as a benchmark for comparison. The _GFLOPs_ mentioned above is used to measure the computational complexity of a model, and \(1 GFLOPs=1\times 10^{9}\)_FLOPs_, so lower a _GFLOPs_ reduces the latency of the neural network, thereby improving their computational speed. Experiments 2 and 3, without increasing the parameters, respectively, increased the \(mAP\) values by 2.7% and 3.7%, and in experiment 2, by introducing the HardSwish activation function, the inference time is lower than the other ones that do not use the HardSwish activation function, which verifies that the characteristic of the segmentation function greatly reduces the computational cost of the model. In Experiment 4, the introduction of the RFB feature pyramid reduces the parameters by 8.79% compared to the SPPCSPC module in YOLOv7 while simultaneously improving the \(mAP\) by 5.2 percentage points. Experiment 5 utilizes the CARAFE lightweight upsampling module in the feature fusion module, and the \(mAP\) value goes to 79.8% with only a slight increase in parameters and _GFLOPs_, which is a significant improvement. In order to verify the lightweight effect of the PConv, Experiment 6 integrates HardSwish, Wise-IOU v3, RFB and CARAFE into YOLOv7. By comparing Experiment 7 with Experiment 6, Our findings reveal a reduction in the network's parameters by 11.85% and computations by 19.46%. Meanwhile, there is a 1.9% increase in the \(mAP\) value. In addition, the inference time of PRC-Light YOLO is 6.62 ms less than that of YOLOv7. The variation curves of \(mAP\) values in seven groups of experiments are shown in Figure 8. Compared with YOLOv7, the number of parameters and _GFLOPs_ of PRC-Light YOLO are reduced by 18.03% and 20.53% respectively, while the \(mAP\) value is improved by 7.6%. Experiments show that by improving the network structure of YOLOv7, the lightweight effect is significant; moreover, the efficacy of defect detection in fabrics using the enhanced model has significantly improved.