## 6 Conclusions  
The best models of YOLOv3 with/without pretrained weights with the highest F1 score pointed out a good trade-off between precision and recall. From the results obtained, it is also evident that the YOLOv3 models, in general, are resilient to the initial weights, perform well with just 20 epochs of training, and have negligible differences in terms of performance with or without pretrained weights. In the case of YOLOv5 models, more epochs are required to obtain a model with good performance when training without any pretrained weights, which is evident from the drastic difference in performance between models trained with and without pretrained weights for 20 epochs. Thus, we can conclude that YOLOv3 architecture is suitable for applications where training resources for training a new model on a new dataset for a customized application are limited, and no pretrained weights are available. The custom dataset comprised a total of 5945 images, out of which 4482 images were used as the training set and 1463 images were used as a test set. With the quantity of images in the dataset, a high degree of accuracy was obtained. This reflects the high quality of the model proposed. The dataset used was generated firsthand, thereby creating a custom dataset which covers all the aspects needed for training and testing the model so proposed. In the future, a more extensive dataset can be generated by exploiting more resources as per feasibility to obtain a more robust model. Compared to training the dataset with the YOLOv5 architecture without any pretrained weights, YOLOv3 provides better results for both F1 Score and mAP in this particular dataset for the ADAS application. This was tested using a dataset that included various object classes detected in the video feed, including four-wheel vehicles, two-wheel vehicles, pedestrians, stray animals, road damage, unmarked speed bumps, and barricades. These models were deployed into a mobile application on the target ADAS prototype hardware. From the analysis of the various models, we can conclude that the models performed and generalized reasonably well on the collected dataset. [PERSON] et al. 2022 proposed a subjective assessment for testing L2 and L2+ ADAS systems which, if used to compare the proposed system with the vision-based features of existing ADAS systems, could further provide better insights [20]. For automotive aftermarket ADAS designers and manufacturers that use computer vision-based AI algorithms to accomplish some functionality, a significant amount of their budget and time is spent on the development and testing of the model. To improve the adoption of these systems, they must be affordable, and manufacturers can do this by using transfer learning-based models (YOLO models with pretrained weights) to suit their target application and reduce the model development time with a slight trade-off in the form of accuracy. The models without pretrained weights have performed as well as their counterparts using pretrained weights, but the trade-off is increased training time. The ADAS system proposed in this module can alert the vehicle drivers of any obstacles. Figure 21: Comprehensive results of YOLOv5 model with the pretrained weights. The alerting mechanism adopted in this system is a buzzer along with visual feedback in the mobile application. This ADAS system can be a life saver in cases when there is a lapse in concentration from the driver's end. Such systems can help prevent road accidents on a large scale and prevent loss of life as well as vehicle damage. Car manufacturers can also think about adding the ADAS system as a feature in the infotainment systems of their cars. This ADAS feature can prove to be a valuable addition to car manufacturers. Conceptualization, K.J. ; Data curation, C.N.J., G.P.A., A.J.S. and K.S. ; Formal analysis, H.V. and K.J. ; Investigation, C.N.J., A.J.S., K.S. and K.J. ; Methodology, G.P.A. and A.J.S. ; Project administration, K.J. ; Resources, H.V. and R.K.R. ; Software, H.V., K.S. and R.K.R. ; Validation, P.R.