# Mobile Robot Tracking with Deep Learning Models under the Specific Environments
## 5 Conclusions  
This paper provided a comparative study of Faster RCNN, SSD and YOLOv5 for mobile robot tracking. All three deep learning models are adaptive to partial occlusion of different degrees as analyzed. We apply these models to specific experiments including erased situations, darkest environment, brightest environment, clear environment, complex environment and variation of occlusions. The performance of the models was demonstrated in terms of F1 score, precision, recall, floating points of operations and training time. A variable \(P\) was created to compare the performance of the models. Four high-quality data sets were generated to facilitate the performance evaluation. The random erasing method and occlusion from different angles were applied to augment the data sets covering various scenarios of partial occlusion.  
Experimental results showed that the SSD model has the best performance and is most promising for the addressed application. The \(P\) score of the SSD model achieved 0.387 in the first testing experiment which is higher than the Faster RCNN method by about 14 times. The \(P\) score of SSD was higher than the YOLO model by about four-fold in the first testing experiment. In the second testing experiment, the \(P\) score of SSD also had a consistent performance. It achieved a score of 0.353, which is higher than the Faster RCNN by about 12 times. It was also higher than the YOLOv5 model by about threefold.  
The analysis revealed different features of the implementation of the three models. The performance of the models differs in terms of the anti-interference ability, detection speed and the ability to determine the object according to the detection of local features. The analysis results may serve as a reference for the model selection to meet specific design metrics. There is also much room for improvement with the current algorithm design and performance evaluation. For example, the images of the current robot data set contain a single robot target. Further tests can be carried out to include more robots in the scene.  
Figure 8: Three models’ testing results in the same frame.  
In the future work, we will improve our data sets. The size of current data sets is small and the diversity of the backgrounds should be expanded. The performance of the current deep learning algorithms is not satisfactory in the detection of small objects. To address the issue, the deeper layers and shallower layers can be concatenated to enrich the semantic information of the shallower layers. In addition, some deep learning models have the structure of a single stage. They can be developed into a multi-stage solution which can increase the precision of detection and localization of small objects.  
Conceptualization, T.Z., M.L.-B., E.L., F.M. and L.Y.; Data curation, Y.S., Z.K. and T.G.; Funding acquisition, E.L., F.M. and L.Y.; Methodology, T.Z., Y.S., Z.K. and T.G.; Resources, T.Z., M.L.-B., E.L., F.M. and L.Y.; Software, Y.S. and Z.K.; Supervision, M.L.-B., E.L., F.M. and L.Y.; Validation, T.Z. and T.G.; Writing--original draft, T.Z., Y.S. and Z.K.; Writing--review & editing, T.G., M.L.-B., E.L., F.M. and L.Y. All authors have read and agreed to the published version of the manuscript.  
This research was partially funded by the Research Enhancement Fund of XJTLU (REF-19-01-04), the National Natural Science Foundation of China (NSFC) (Grant No. 61501380), and by AI University Research Center (AI-URC) and XJTLU Laboratory for Intelligent Computation and Financial Technology through XJTLU Key Programme Special Fund (KSF-P-02), Jiangsu Data Science and Cognitive Computational Engineering Research Centre, and ARIES Research Centre and Suzhou Key Lab of Broadband wireless Access Technology (BWAT).  
Not applicable.  
The authors declare no conflict of interest.