# One For All: A Mutual Enhancement Method for Object Detection and Semantic Segmentation
## 5 Results Analysis  
### How Does Detection Affect Segmentation?  
Firstly, the model pays attention to the impacts on segmentation performance. To figure out the influences on segmentation task by mixing detection data, the IOU index is collected over training epochs. The results are presented in Figures 2 and 3. The legend \"1-N\" means the \"1-N Alternation\" strategy is employed to train the model. The models were trained and evaluated on the train and validation sets specified in Table 1.  
Figure 2: IOU (intersection over union) of road segmentation over epochs.  
Figure 2 displays the results of mixing road segmentation data with detection data, and the IOUs are evaluated when \(N\) is equal to 0, 1, 2, and 5. What is discovered is that by adding detection images as an additional training data source, the model performs better anyway. Specifically, when joining detection images, the model converges fairly faster compared with training only with road segmentation data, especially during the first few epochs. To be more specific, considering the performance on the validation set, the first epoch of the \"1-2 Alternation\" model performs almost comparable to the 10 th epoch of the \"1-0 Alteration\" (without detection data). This phenomenon tells the fact that the features for object detection have a strong correlation with the segmentation task. Figure 2 also shows that the \"1-2 Alternation\" model got about 2% improvement compared with the \"1-0 Alternation\" model. Another thing observed is that the \"1-2 Alternation\" model behaves a little better than the \"1-1 Alternation\" model. So, is it beneficial for a bigger \(N\) all the time? To solve this question, \(N\) was increased to 5 but the result shows that it is not always better for a bigger \(N\). If \(N\) is too big, the IOU curve will fluctuate over epochs and sometimes performs even worse than the model without detection data.  
Figure 3 describes the results of mixing pixel-wise segmentation data with object detection data. Let \(N\) be equal to 0, 1, 2, and 5 again, the model produces similar results found in Figure 2. And considering the aim of KITTI pixel-wise segmentation is to pick fine area of objects like cars, persons and so on, the distance between this task and detection task is shorter than that of road segmentation and detection tasks, and as a consequence of which, the improvement of IOU is more obvious (approximately 9% improvement).  
Based on the findings mentioned above, a suggestion is that when the segmentation dataset is limited or difficult to collect due to high labeling cost or some other reasons, people should try to label some detection data with the amount of one or two times of the segmentation data. By adding these detection data to the training processes, people should get a much better result. From this aspect, the \"1-N Alternation\" strategy can be viewed as a data augmentation methodology. Notice that in Figures 2 and 3 the IOUs on the training sets are very similar regardless of the value of \(N\), whereas  
Figure 3: IOU (intersection over union) of pixel-wise segmentation over epochs.  
the IOUs on the validation sets change a lot due to different values of \(N\), which means the model generalizes better when \(N\) is appropriate. Therefore, the strategy designed in the OFA-Net model can be seen as a regularization method as well.