# A Lightweight and Efficient Detection Transformer for Highway Abandoned Objects
## 3 Experimental Results and Performance Analysis
### Verifying That RepELAN Is More Efficient
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Module** & **Feature Map Size** & **Params (M)** & **FLOPs (G)** & **FPS** \\ \hline \multirow{6}{*}{C3} & 320 \(\times\) 320 \(\times\) 64 & 0.019 & 15.1 G & 90.7 \\  & 160 \(\times\) 160 \(\times\) 128 & 0.074 & 15.1 G & 149.8 \\  & 80 \(\times\) 80 \(\times\) 256 & 0.296 & 15.1 G & 202.6 \\  & 40 \(\times\) 40 \(\times\) 512 & 1.181 & 15.1 G & 241.7 \\  & 20 \(\times\) 20 \(\times\) 1024 & 4.722 & 15.1 G & 229.7 \\ \hline \multirow{6}{*}{ELAN} & 320 \(\times\) 320 \(\times\) 64 & 0.031 & 25.2 G & 76.7 \\  & 160 \(\times\) 160 \(\times\) 128 & 0.123 & 25.2 G & 118.0 \\  & 80 \(\times\) 80 \(\times\) 256 & 0.492 & 25.2 G & 153.6 \\  & 40 \(\times\) 40 \(\times\) 512 & 1.968 & 25.2 G & 169.9 \\  & 20 \(\times\) 20 \(\times\) 1024 & 7.867 & 25.2 G & 147.7 \\ \hline \multirow{6}{*}{C2f} & 320 \(\times\) 320 \(\times\) 64 & 0.029 & 23.5 G & 71.3 \\  & 160 \(\times\) 160 \(\times\) 128 & 0.115 & 23.5 G & 107.6 \\ \cline{1-1}  & 80 \(\times\) 80 \(\times\) 256 & 0.460 & 23.5 G & 147.9 \\ \cline{1-1}  & 40 \(\times\) 40 \(\times\) 512 & 1.837 & 23.5 G & 175.6 \\ \cline{1-1}  & 20 \(\times\) 20 \(\times\) 1024 & 7.343 & 23.5 G & 155.4 \\ \hline \multirow{6}{*}{RepNCSPELAN} & 320 \(\times\) 320 \(\times\) 64 & 0.014 & 11.5 G & 57.4 \\ \cline{1-1}  & 160 \(\times\) 160 \(\times\) 128 & 0.057 & 11.5 G & 116.4 \\ \cline{1-1}  & 80 \(\times\) 80 \(\times\) 256 & 0.226 & 11.5 G & 185.8 \\ \cline{1-1}  & 40 \(\times\) 40 \(\times\) 512 & 0.903 & 11.5 G & 245.0 \\ \cline{1-1}  & 20 \(\times\) 20 \(\times\) 1024 & 3.608 & 11.5 G & 252.4 \\ \hline \multirow{6}{*}{RepELAN (ours)} & 320 \(\times\) 320 \(\times\) 64 & 0.013 & 10.7 G & 93.7 \\ \cline{1-1}  & 160 \(\times\) 160 \(\times\) 128 & 0.053 & 10.7 G & 161.8 \\ \cline{1-1}  & 80 \(\times\) 80 \(\times\) 256 & 0.210 & 10.7 G & 225.1 \\ \cline{1-1}  & 40 \(\times\) 40 \(\times\) 512 & 0.837 & 10.7 G & 291.3 \\ \cline{1-1}  & 20 \(\times\) 20 \(\times\) 1024 & 3.345 & 10.7 G & 298.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison between the RepELAN module and other state-of-the-art modules of the same type. model increased by 44.6%, and the YOLOv8-N model saw an 18.8% increase. These improvements further demonstrate the potential of the RepELAN module in enhancing real-time performance. Regarding detection accuracy, models using RepELAN demonstrated stable or improved performance in their AP50 and AP50:95 metrics. For instance, the AP50:95 of the YOLOv5-N model increased from 33.3% to 35.1%, and the AP50 of the YOLOv9-C model rose from 78.7% to 81.2%. Overall, replacing the original feature extraction modules with RepELAN significantly reduces the parameter count and FLOPs across all models while also improving inference speed. Moreover, the AP values for most models are either maintained or slightly improved. For example, in YOLOv8-N, replacing C2f with RepELAN results in a 24.7% reduction in FLOPs (from 8.1 G to 6.1 G), with almost no change in accuracy (AP50:95 increases from 37.3% to 37.2%). The experimental results show that the RepELAN module performs better than other state-of-the-art feature extraction modules in both model efficiency and detection performance, proving its design is effective.