## 4 Experiment and Results
### Experimental Result
#### 4.4.3 Experiment on Exploring the Effect of LFE Blocks Inserted into the Network  
Experimental results carried out on the original YOLOv4 and the improved YOLOv4 with 5 LFE blocks inserted under the CPU are shown on the left side of Table 2. Compared with the original YOLOv4, the \(mAP\) of the improved YOLOv4 on all three datasets was improved. Due to the small number of samples in the PCB dataset itself, \(mAP\) was relatively small. However, results were still improved, indicating that the LFE module was also effective for other datasets and had good generalization ability. Meanwhile, in the GPU environment, one LFE block was integrated into YOLOv4 at the third location in Figure 3, and experiments were conducted on three different datasets for comparison. The experimental results are shown on the right side of Table 2. Through the verification of two different experimental environments, it can be proven that the location feature extraction structure can improve network accuracy no matter the GPU or CPU conditions. In addition, the LFE module is universal across different datasets, which can prove the existence of generality.  
4.4 The Joint Experiment of Self-Supervised Self-Knowledge Distillation and LFE Block under GPU Condition  
In addition to the above independent network exploration, a joint experiment of self-knowledge distillation, pre-training, and downstream tasks was conducted. Improved self-distillation was used to do pre-training on the Simdis2x network and was then trained and tested on the Pascal VOC, PCB, and our small target dataset. At the same time,  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{CPU} & \multicolumn{3}{c}{GPU} & \multicolumn{1}{c}{-} \\ \hline \multirow{2}{*}{**Network**} & **Training** & \multirow{2}{*}{_[EMAIL_ADDRESS]} & \multirow{2}{*}{**Network**} & **Training** & \multirow{2}{*}{_[EMAIL_ADDRESS]} & \multirow{2}{*}{_[EMAIL_ADDRESS]:95_} & \multirow{2}{*}{**Dataset**} \\  & **Parameters** & & & & & \\ \hline YOLOv4 & Epoch = 50 & 43.65\% & YOLOv4 & Epoch = 50 & 80.2\% & 58.1\% \\  & Batch size = 32 & & Batch size = 32 & & & \\ YOLOv4 + Five & Epoch = 50 & **45.45\%** & YOLOv4 + one & Epoch = 50 & **81.6\%** & **60\%** & \\ LFE blocks & Batch size = 32 & & LFE block & Batch size = 32 & & \\ \hline YOLOv4 & Epoch = 50 & 36.29\% & YOLOv4 & Epoch = 150 & 62\% & 24.7\% & \\ YOLOv4 + Five & Epoch = 50 & **38.89\%** & YOLOv4 + one & Epoch = 150 & **67.3\%** & **27.7\%** & PCB \\ LFE blocks & Batch size = 32 & & LFE block & Batch size = 64 & & \\ \hline YOLOv4 & Epoch = 50 & 84.24\% & YOLOv4 & Epoch = 100 & 82.1\% & 57.1\% & Pascal VOC \\ YOLOv4 + Five & Epoch = 50 & **84.47\%** & YOLOv4 + one & Epoch = 100 & **82.6\%** & **58.7\%** & 07 + 12 \\ LFE blocks & Batch size = 32 & & LFE block & Batch size = 64 & & \\ \hline \hline \end{tabular}  
* The bold font in the table is the better result in each group of comparison. Same as Tables 3 and 4.  
\end{table}
Table 2: \(mAP\) of original YOLOv4 and improved YOLOv4 in different environments.  
Figure 6: Proportion of \(FP\) in different categories.  
during the downstream task training, the backbone of the training network was changed to ResNet18. Table 3 shows our experimental results.  
Comparing the experimental results of different groups, it can be concluded that the proposed self-distillation and LFE modules have improved the network's \(mAP\), respectively, and that the combined use of the two modules is the best. The location feature extraction strategy is also suitable for self-supervised scenarios and has advantages for the improvement of downstream tasks.  
In addition, after inserting the module into the two latest baseline networks [15; 63], the detection accuracy was improved compared with the original networks in Table 4, which proves the effectiveness of the two LFE blocks.