# A Lightweight and Efficient Detection Transformer for Highway Abandoned Objects
## 3 Experimental Results and Performance Analysis
### Comparison to State-of-the-Art Models  
To evaluate the proposed method's performance advantages, we conducted experiments using the LE-DETR model on the HAOD and compared its performance to that of Faster R-CNN [37], DETR [38], Deformable-DETR [39], DINO-Deformable-DETR [40], YOLOv5, YOLOv7, YOLOv8, YOLOv9, and RT-DETR.  
The experimental results in Table 7 demonstrate that LE-DETR attained a comprehensive accuracy (AP50:95) of 42.6, surpassing all other models in the comparison and showcasing its superior detection performance. Specifically, the AP50 of LE-DETR was 84.3, which was higher than the 4.6 and 3.4 percentage points of the end-to-end detection model DINO-Deformable-DETR-R50 and the classical two-stage model Faster R-CNN-R50, respectively. Furthermore, LE-DETR displayed balanced and exceptional performance in detecting abandoned objects of various sizes, particularly excelling in medium-sized object detection by outperforming the suboptimal model by 5.5%. Notably, LE-DETR, a model based on the DETR architecture, not only guarantees real-time capabilities but also exhibits a higher level of detection accuracy than the YOLO series of real-time detection models. These experiments validate the effectiveness and superiority of LE-DETR in abandoned object detection. Its efficient performance and economical computational costs render it more suitable for practical applications and deployment.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Bas.**} & \multirow{2}{*}{**RLNet**} & \multirow{2}{*}{**TFusion**} & \multirow{2}{*}{**CMI**} & **Params** & **FLOPs** & **FPS** & **AP** & **AP50** & **AP75** & **AP\_s** & **AP\_m** & **AP\_1** \\  & & & **(M)** & **(G)** & **(bs = 8)** & **50:95** & **AP50** & **AP75** & **AP\_s** & **AP\_m** & **AP\_1** \\ \hline \(\surd\) & & & 19.9 & 57.0 & 86.2 & 40.0 & 82.5 & 31.7 & 20.1 & 43.3 & 34.9 \\  & \(\surd\) & & 10.4 & 33.1 & 100 & 40.8 & 84.2 & 32.9 & 21.6 & 44.1 & 35.4 \\  & \(\surd\) & \(\surd\) & & 10.4 & 35.2 & 91.7 & 41.4 & 84.2 & 34.3 & 21.4 & 44.5 & 36.9 \\  & \(\surd\) & \(\surd\) & \(\surd\) & 14.5 & 45.0 & 79.3 & 42.6 & 84.3 & 37.4 & 21.3 & 45.7 & 38.6 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Results of ablation experiments; \(\surd\) means that this component has been applied. Our baseline is indicated by Bas.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Epochs**} & \multirow{2}{*}{**Params** & **FLOPs** & **FPS** & **AP50:95** & **AP50** & **AP75** & **AP\_s** & **AP\_m** & **AP\_1** \\  & & & **(M)** & **(G)** & **(bs = 8)** & **AP50:95** & **AP50** & **AP75** & **AP\_s** & **AP\_m** & **AP\_1** \\ \hline Faster R-CNN-R50 & 12 & 41.294 & 202 & - & 38.0 & 82.9 & 27.4 & 20.6 & 41.0 & 33.4 \\ \hline DETR-R18 & 500 & 28.832 & 48.372 & - & 31.4 & 77.4 & 18.2 & 9.0 & 34.6 & 29.0 \\ Deformable-DETR-R50 & 50 & 40.101 & 187 & - & 38.1 & 82.9 & 27.4 & 22.3 & 40.5 & 35.4 \\ DINO-Deformable-DETR-R50 & 12 & 47.559 & 266 & - & 39.2 & 82.9 & 30.5 & 18.1 & 42.8 & 32.9 \\ RT-DETR-R18 & 150 & 20.0 & 57.3 & 86.2 & 40.0 & 82.5 & 31.7 & 20.1 & 43.3 & 34.9 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison to state-of-the-art models. The inference speeds of the Faster R-CNN-R50, DETR-R18, Deformable-DETR-R50, and DINO-Deformable-DETR-R50 were not tested because they are not real-time detectors.