## 4 Results and Discussion
### Automated Detection
However, these are not completely misplaced: there is a reflection of the rear view mirror (pink SFL.MrBs bounding box) on the left side of the vehicle, the rear view mirror on the right side (blue and orange SFL.MrBs boxes) is visible through the windows, one of the headsets (white SFL.MrBs box) looks similar to the back of a rear view mirror, and the right door pillar (red SFL.DrPi box) is visible through the windows. In fact, side views of the vehicles are currently the most challenging, since these are the most discrepant from  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Acc. **} & \multirow{2}{*}{**Prec. **} & \multicolumn{4}{c}{**Label-Based**} & \multicolumn{4}{c}{**Example-Based**} \\  & & & & **Rec. ** & & & & **F1** & **MCC** & **Acc. ** & **Prec. ** & **Rec. ** & **Hamm. (\downarrow)** & **F1** & **F1** & **F1** \\  & & & & & & & & & & & & & & & & & \\  & & & & & & & & & & & & & & & & & \\ \hline \multicolumn{11}{c}{Detectron2 Macro Categories} \\ Baseline [10] & 0.656 & 0.462 & 0.647 & 0.419 & 0.449 & 0.394 & 0.530 & **0.198** & 0.284 & 0.357 & 0.647 & 0.446 & 0.344 & 0.528 & - & - \\ CycleGAN [30] & 0.652 & 0.491 & 0.594 & 0.550 & 0.616 & 0.455 & 0.605 & 0.166 & 0.294 & 0.435 & 0.600 & 0.617 & 0.348 & 0.609 & 15.34 & 8.10 \\ \hline New Baseline & 0.642 & 0.800 & 0.954 & 0.535 & 0.581 & 0.599 & 0.722 & 0.093 & 0.372 & 0.576 & 0.968 & 0.596 & 0.358 & 0.735 & - & - \\ CycleGAN & 0.692 & 0.730 & 0.965 & 0.585 & 0.640 & 0.623 & 0.769 & 0.170 & 0.435 & 0.635 & **0.964** & 0.653 & 0.308 & 0.778 & 5.850 & 4.30 \\ CUT & 0.753 & **0.898** & 0.951 & 0.683 & 0.729 & 0.734 & 0.826 & 0.134 & 0.473 & 0.706 & 0.950 & 0.738 & 0.247 & 0.830 & 12.93 & 9.50 \\ CUT + PL & 0.794 & 0.870 & 0.914 & 0.812 & 0.821 & 0.808 & 0.865 & 0.173 & 0.450 & 0.759 & 0.911 & 0.824 & 0.206 & 0.865 & 17.69 & 13.0 \\ CUT + PL + FCS & **0.857** & 0.874 & 0.923 & **0.889** & **0.896** & **0.886** & **0.909** & **0.218** & **0.569** & **0.830** & 0.919 & **0.899** & **0.143** & **0.909** & **23.70** & **17.4** \\ \hline \multicolumn{11}{c}{CIR Final Codes} \\ \hline Baseline [10] & 0.709 & 0.295 & 0.438 & 0.322 & 0.359 & 0.264 & 0.395 & 0.154 & 0.207 & 0.244 & 0.426 & 0.356 & 0.291 & 0.388 & - & - \\ CycleGAN [30] & 0.671 & 0.284 & 0.401 & 0.428 & 0.498 & 0.293 & 0.444 & 0.140 & 0.217 & 0.289 & 0.403 & 0.497 & 0.329 & 0.445  
the simulated image (in most simulated images of the side of the car more than 50% of the vehicle is visible, while in the real images the perspective includes only the front or the back halves of the car separately). We wish to correct this in future versions of the simulated data if the collection of real images continues to be done in this mobile scenario. Another alternative is the fixed camera setup described in Section 3.3.4, whose simulation improved the example-based F1-score from 0.565 to 0.589 and 0.601, before and after post processing, respectively. The results can be observed in the last column of Figure 7, where there is a correction of the left/right mismatches (e.g., on the first row the light blue bounding box of the car rim SFR.Tr was switched to SFL.Tr) and a decrease in the number of detected objects, especially on the 4 th row. This confirms the viability of the solution, especially considering that we are interested in solving this specific problem for this particular factory environment, as opposed to finding a general approach.