## 4 Experimental Results and Analysis
### Ablation Experiments with Sequential-Differential Acceleration and Convolutional Pooling  
To verify the improvement of detection accuracy by false-alarm-object learning and the multi-anchor assignment strategy, ablation experiments were conducted, and the results are shown in Table 2.  
The multi-anchor assignment strategy increased the precision rate from 97.79% to 98.64% and improved the recall rate from 82.66% to 87.61%; the false-alarm-object learning further enhanced the precision rate from 98.64% to 99.17% and boosted the recall rate from 87.61% to 90.18%.  
To verify the improvement of the network performance by the sequential-differential acceleration method and convolutional pooling, ablation experiments were conducted. Differential acceleration and convolutional pooling are applied to the LSDF-Net. LSDF-Net + Diff-Acc is converted from the Baseline LSDF-Net by the sequential-differential acceleration method, and LSDF-Net + Diff-Acc + Conv-Pl is retrained with the convolutional pooling method. The networks are deployed to the heterogeneous FPGA JFMQL100 TAI using the same settings. The results of the experiment are shown in Table 3.  
In the table, the hardware inference time is divided into two parts. One is hardware computation time, which represents the time spent by the hardware only on computation, such as convolution operation time and activation time; the other is memory copy time, which refers to the memory access and data transfer time. The average data in the table are the average values of four groups of validation sets. The results indicate that the implementation of sequential-differential acceleration leads to a reduction in memory copy time and hard time. Specifically, the average inference time decreases by 11.37%. Additionally, after applying convolutional pooling, the average inference time is further reduced by 15.78%. Notably, all three networks maintain a consistent recall rate performance.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Ablation Technique** & **Precision Rate** & **Recall Rate** \\ \hline Original network & 97.79\% & 82.66\% \\ Original network + Multi-anchor assignment strategy & 98.64\% & 87.61\% \\ Original network + Multi-anchor assignment strategy & 99.17\% & 90.18\% \\ + False-alarm-object learning & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Ablation study results of the network.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Ablation Technique** & **Para** /k** & **Datasets** & **Recall Rate** & \begin{tabular}{c} **Memory Copy** \\ **Time/μs** \\ \end{tabular} & \
\begin{tabular}{c} **Hardware** \\ **Computation** \\ **Time/μs** \\ \end{tabular} &
\begin{tabular}{c} **Hardware** \\ **Inference** \\ **Time/μs** \\ \end{tabular} & **Increase** \\ \hline \multirow{4}{*}{LSDF-Net} & \multirow{4}{*}{98.5} & 1 & 100\% & 6754 & 3573 & 10,327 & \multirow{4}{*}{-} \\  & & 2 & 100\% & 6768 & 3555 & 10,323 & \multirow{4}{*}{-} \\  & & 3 & 94.12\% & 6785 & 3621 & 10,406 & \\  & & 4 & 85.71\% & 6809 & 3655 & 10,464 & \\  & & average & 94.96\% & 6779 & 3601 & 10,380 & \\ \hline \multirow{4}{*}{LSDF-Net + Diff-Acc} & \multirow{4}{*}{98.9} & 1 & 100\% & 6155 & 3060 & 9215 & \multirow{4}{*}{11.37\%} \\  & & 2 & 100\% & 6201 & 3099 & 9300 & \\ \cline{1-1}  & & 3 & 94.12\% & 6188 & 2987 & 9175 & \\ \cline{1-1}  & & 4 & 85.71\% & 5936 & 3174 & 9110 & \\ \cline{1-1}  & & average & 94.96\% & 6120 & 3080 & 9200 & \\ \hline \multirow{4}{*}{LSDF-Net + Diff-Acc + Conv-Pl} & \multirow{4}{*}{98.4} & 1 & 100\% & 6021 & 2719 & 8740 & \multirow{4}{*}{15.78\%} \\  & & 2 & 100\% & 6003 & 2635 & 8638 & \\ \cline{1-1}  & & 3 & 94.12\% & 5988 & 2698 & 8686 & \\ \cline{1-1}  & & 4 & 85.71\% & 6152 & 2756 & 8908 & \\ \cline{1-1}  & & average & 94.96\% & 6041 & 2702 & 8743 & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Ablation study results.