# A Real-Time Car Towing Management System Using ML-Powered Automatic Number Plate Recognition
## 6 Conclusions  
This paper presented the design and implementation of an ML-powered car towing management system using automatic number plate recognition. We believe that the proposed system would create a better opportunity for law-enforcement personnel to automate the process of car towing. The proposed ANPR model approach comprises three phases: plate detection, character segmentation, and character recognition. We tested our system with an image dataset of various shaped and sized number plates, where crowded backgrounds, low contrast, and diverse illumination condition images are taken into consideration.  
We carried out several sets of experiments for evaluating the performance and classification accuracy of our system, paying particular attention to the classification and processing time. Most notably, our model could process a number of images per second more than triple the commercial fps. This proves that our ANPR model is suitable for real-time inference at the edge with high prediction accuracy and response time, which could be used for various applications such as tolling, traffic management, security surveillance, etc. Moreover, we found that our model outperforms some state-of-the-art ANPR approaches in terms of the overall processing time.  
We expect that this research would increase the open-source knowledge base in the area of computer vision and real-time video surveillance on the network edge by publishing the source code and dataset to the public domain (The source code and dataset are available online: [[https://github.com/ahmed-pvamu/Car-Towing-Management](https://github.com/ahmed-pvamu/Car-Towing-Management)]([https://github.com/ahmed-pvamu/Car-Towing-Management](https://github.com/ahmed-pvamu/Car-Towing-Management)), accessed on 10 September 2021).  
In on-going work, we are looking into opportunities for generalizing our approach to be used for commercial surveillance cameras. We are developing a motion detection module to be integrated with our model to capture an image when a new scene is detected. It will be useful for supporting the sensing needs of a wide range of researches [25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35] and applications [36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46]. Finally, experiments with more massive datasets are needed to study the robustness of our system at a large scale, and improve the prediction accuracy and minimize the character mismatch errors.  
Conceptualization, A.A.A. and S.A.; methodology, A.A.A.; software, S.A.; validation, A.A.A.; formal analysis, A.A.A.; investigation, A.A.A. and S.A.; resources, A.A.A.; data curation, S.A.; writing--original draft preparation, A.A.A. and S.A.; writing--review and editing, A.A.A.; visualization, S.A.; supervision, A.A.A.; project administration, A.A.A.; funding acquisition, A.A.A. All authors have read and agreed to the published version of the manuscript.  
Figure 10: The CPU processing time of our model compared to some existing models such as SVM and YOLO.  
**Funding:** This research work is supported in part by the National Science Foundation (NSF) under grant # 2011330. Any opinions, findings, and conclusions expressed in this paper are those of the authors and do not necessarily reflect NSF's views.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data and source code that support the findings of this study are openly available at: [[https://github.com/ahmed-pvamu/Car-Towing-Management](https://github.com/ahmed-pvamu/Car-Towing-Management)]([https://github.com/ahmed-pvamu/Car-Towing-Management](https://github.com/ahmed-pvamu/Car-Towing-Management)) (accessed on 16 October 2021).  
**Conflicts of Interest:** The authors declare no conflict of interest.