# Part-Based Obstacle Detection Using a Multiple Output Neural Network
## 6 Conclusions  
In this paper, we propose a solution that is capable of accurately detecting on-road obstacles and their actual instances in various road-traffic scenarios. We achieved this by leveraging encoder-decoder-based artificial networks and geometry-based computer vision algorithms. The information about object parts was extracted using semantic segmentation networks and then used in a low-complexity clustering algorithm. Therefore, our proposed solution is capable of detecting individual objects, even if they are partly occluded or are in close contact. The multi-output CNN model, together with the post-processing algorithm, represents a different approach to the traditional object segmentation and detection problem. The system is also lighter in terms of computational demand and is easier to train, while also having an accuracy comparable with much more complex artificial neural networks. We have presented a unique approach to detecting object instances, along with the semantic segmentation of the scene. In this approach, we fuse the two prediction outputs to obtain better object instances; we also predict information regarding the vanishing point that can be used later to compute the extrinsic camera parameters. In conclusion, the proposed approach features a lower number of parameters than in similar published work, has similar or better performance than previous approaches when evaluating instances and the 2D bounding boxes, and is easy to train and deploy. The proposed solution can be used in real-time systems with a single camera to predict individual obstacle instances in road-traffic scenarios, while also predicting the vanishing point, which can be used for the self-calibration of the camera. Our proposed solution has been evaluated using well-established performance indicators on publicly available datasets and our own acquired database.  
Future work will include using these results along with a tracker, in order to estimate the trajectories of the road participants. The idea is to make use of multiple consecutive frames, in order to better determine partly or fully occluded objects from the road scene. This future work will also include exploring other prediction outputs for the CNN model, such as depth or optical flow.  
Conceptualization, R.I. and R.D.; methodology, R.I. and R.D.; software, R.I. and R.D.; validation, R.I. and R.D.; writing--original draft preparation, R.I. and R.D.; writing--review and editing, R.I. and R.D. All authors have read and agreed to the published version of the manuscript.  
This work was partly supported by grants from the Ministry of Research and Innovation, CNCS--UEFISCDI, project number PN-III-P4-ID-PCE2020-1700 and PN-III-P1-1-1-PD-2021-0247, and partly supported by the project \"Entrepreneurial competencies and excellence research in doctoral and postdoctoral programs--ANTREDOC\", which project was co-funded by the European Social Fund, financing agreement no. 56437/24.07.2019.  
Not applicable.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Solution**} & **CityScapes** & **VP Highway** & \multirow{2}{*}{**VP City (NormDist)**} \\  & **(NormDist)** & **(NormDist)** & \\ \hline MONet V1 [39] & - & 0.050 & 0.026 \\ Proposed system & 0.016 & 0.013 & 0.018 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Vanishing point evaluation.