# Machine Learning-Based Highway Truck Commodity Classification Using Logo Data
## 5 Summary and Conclusions  
A vision-based freight classification approach has been proposed in the present paper. Our proposed solution consisting of text-based and image-based branches is able to capture most existing logos. Both our text-based and image-based solutions are general and can easily be extended to new logo classes. Our text-based approach using advanced scene-text solutions produces highly accurate predictions when the logo is mostly text. To extend it to new logo classes, only the text strings of the logos are needed. Our image-based approach serves as a complement and deals with logos with little or no text. It first detects all potential logos, and then performs feature matching with samples of different logo classes. For potential new logo classes, only around 30 samples need to be collected per class. Furthermore, we have developed a new freight classification benchmark based on logo data. To our best knowledge, ours is the first dataset collected to evaluate freight classification based on logo data. It can be useful in providing traffic engineers and researchers with a dataset to systematically evaluate their developed freight classification models.  
We showed through experiments that our overall accuracy of 80% for 26 chosen logos is very promising. However, the logos we found from recorded highway videos (from the state of Florida) are not exhaustive, and the current study is limited by the size of the collected dataset. To further improve the accuracy and make our approach feasible in  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Image-Based**} & \multicolumn{3}{c}{**Text-Based**} & \multicolumn{3}{c}{**Integrated Approach**} \\ \hline
**Logo Groups** & **Precision** & **Recall** & **Average Precision** & **Precision** & **Recall** & **Average Precision** & **Precision** & **Recall** & **Average Precision** \\ \hline Easy & 74.7 & 88.5 & 84.6 & 90.7 & 95.6 & 91.7 & 86.5 & 98.0 & 98.1 \\ Medium & 60.3 & 83.1 & 69.9 & 93.4 & 90.6 & 88.9 & 72.6 & 95.1 & 90.2 \\ Difficult & 45.4 & 50.8 & 39.5 & 39.9 & 24.5 & 23.4 & 56.2 & 95.5 & 51.4 \\ \hline Overall & 60.7 & 76.1 & 66.2 & 71.5 & 67.3 & 65.6 & 72.5 & 86.4 & 81.2 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Evaluations of the text-based and image-based approaches on three logo groups.  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Text-focused Approach**} & \multicolumn{3}{c}{**Combined Approach**} \\ \hline
**Recall** & **Precision** & **Average Precision** & **Accuracy** & **Recall** & **Precision** & **Average Precision** & **Accuracy** \\ \hline
87.1 & 69.8 & 82.2 & 87.2 & 82.6 & 78.7 & 73.2 & 86.4 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Ablation study of integration approaches.  
automatic commodity classification applications, we state that collective effort is needed to build a comprehensive on-truck logo database, a logo-to-commodity database, as well as popularization of commodity logos printed on trucks.  
Conceptualization, P.H.; Data curation, A.W. and X.H.; Investigation, P.H.; Methodology, P.H. and A.W.; Project administration, A.R. and S.R.; Software, P.H.; Supervision, A.R. and S.R.; Visualization, A.W.; Writing--original draft, P.H. and A.W.; Writing--review & editing, X.H. All authors have read and agreed to the published version of the manuscript.  
This work is supported by NSF CNS 1922782, by the Florida Dept. of Transportation (FDOT) and FDOT District 5. The opinions, findings and conclusions expressed in this publication are those of the author(s) and not necessarily those of the Florida Department of Transportation or the National Science Foundation.  
The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.