# One For All: A Mutual Enhancement Method for Object Detection and Semantic Segmentation
## 5 Results Analysis
### OFA-Net Results
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline
**Benchmark** & **Model** & **MaxF** & **AP** & **PRE** & **REC** & **FPR** & **FNR** \\ \hline \multirow{6}{*}{UM\_ROAD} & OFA-Net & 92.08 \% & 82.73 \% & 87.87 \% & **96.72 \%** & 6.08 \% & **3.28 \%** \\  & MixedCRF & 91.57 \% & 84.68 \% & 90.02 \% & 93.19 \% & 4.71 \% & 6.81 \% \\  & ALO-AVG-MM & 91.15 \% & 83.82 \% & 89.07 \% & 93.33 \% & 5.22 \% & 6.67 \% \\  & HybridCRF & 90.99 \% & 85.26 \% & 90.65 \% & 91.33 \% & 4.29 \% & 8.67 \% \\  & HID-LS & 93.10 \% & 86.38 \% & 91.89 \% & 94.33 \% & 3.79 \% & 5.67 \% \\ \hline \multirow{6}{*}{UMM\_ROAD} & OFA-Net & **95.43** \% & 89.10 \% & 92.78 \% & **98.24** \% & 8.41 \% & **1.76 \%** \\  & MixedCRF & 92.75 \% & 90.24 \% & 94.03 \% & 91.50 \% & 6.39 \% & 8.50 \% \\ \cline{1-1}  & ALO-AVG-MM & 94.05 \% & 90.96 \% & 94.82 \% & 93.29 \% & 5.60 \% & 6.71 \% \\ \cline{1-1}  & HybridCRF & 91.95 \% & 86.44 \% & 94.01 \% & 89.98 \% & 6.30 \% & 10.02 \% \\ \cline{1-1}  & HID-LS & 94.89 \% & 91.46 \% & 95.37 \% & 94.42 \% & 5.04 \% & 5.58 \% \\ \hline \multirow{6}{*}{UU\_ROAD} & OFA-Net & **92.62** \% & **83.12** \% & **88.97** \% & **96.58** \% & 3.90 \% & **3.42 \%** \\  & MixedCRF & 85.69 \% & 75.12 \% & 80.17 \% & 92.02 \% & 7.42 \% & 7.98 \% \\ \cline{1-1}  & ALO-AVG-MM & 89.45 \% & 79.87 \% & 85.40 \% & 93.90 \% & 5.23 \% & 6.10 \% \\ \cline{1-1}  & HybridCRF & 88.53 \% & 80.79 \% & 86.41 \% & 90.76 \% & 4.65 \% & 9.24 \% \\ \cline{1-1}  & HID-LS & 89.81 \% & 82.33 \% & 88.11 \% & 91.58 \% & 4.03 \% & 8.42 \% \\ \hline \multirow{6}{*}{URBAN\_ROAD} & OFA-Net & **93.74** \% & 85.37 \% & 90.36 \% & **97.38** \% & 5.72 \% & **2.62 \%** \\ \cline{1-1}  & MixedCRF & 90.59 \% & 84.24 \% & 89.11 \% & 92.13 \% & 6.20 \% & 7.87 \% \\ \cline{1-1}  & ALO-AVG-MM & 92.03 \% & 85.64 \% & 90.65 \% & 93.45 \% & 5.31 \% & 6.55 \% \\ \cline{1-1}  & HybridCRF & 90.81 \% & 86.01 \% & 91.05 \% & 90.57 \% & 4.90 \% & 9.43 \% \\ \cline{1-1}  & HID-LS & 93.11 \% & 87.33 \% & 92.52 \% & 93.71 \% & 4.18 \% & 6.29 \% \\ \hline \hline \end{tabular}  
* The results surpass other models are labeled in bold. Lower is better in the last two columns. Higher is better in other columns.  
\end{table} 
Table 3: Road segmentation evaluation results [50] *. area). The OFA-Net model here has a very low false-negative rate, thus has a very good ability to recognize road areas, which is helpful for autonomous driving. Figure 4: Visualization of comparisons with other models (each group has the outputs of OFA-Net, MixedCRF, ALO-AVG-MM, HybridCRF, and HID-LS from top to bottom, respectively. Find the meaning of different road types from the research made by [PERSON], [PERSON], etc. [24].) The object detection and pixel-wise semantic segmentation were not tested on the server because just several common categories of them were used to explore the relationships between segmentation and object detection tasks and to find a new training strategy that both tasks can benefit from. For a better intuitive feeling of the object detection effects of the OFA-Net model, some visualizations of the OFA-Net outputs are displayed in Figure 5.  
Figure 5: Visualization of the OFA-Net outputs.