Electrical scooters have gained a lot of attention and popularity among commuters all around the world since they entered the market. After all, electrical scooters have shown to be efficient and cost-effective mode of transportation for commuters and travelers. As of today electrical scooters have firmly established themselves in the micromobility industry, with an increasing global demand.  Although, as the industry is booming so are the accidents as well as getting into dangerous situations of riding electrical scooters. There is a growing concern regarding the safety of the scooters where more and more people are getting injured.   This research focuses on training a computer vision model using semi-supervised learning to help detect traffic rule violations and also prevent collisions for people using electrical scooters. However, applying a computer vision model on an embedded system can be challenging due to the limited capabilities of the hardware. This is where the model can enable post-training quantizations. This thesis examines which post-training quantization has the best performance and if it can perform better compared to the non-quantized model. There are three post-training quantizations that are being applied to the model, dynamic range, full integer and float16 post-training quantizations. The results showed that the non-quantized model achieved a mean average precision (mAP) of 0.03894 with a mean average training and validation loss of 22.10 and 28.11. The non-quantized model was compared with the three post-training quantizations in terms of mAP where the dynamic range post-training quantization achieve the best performance with a mAP of 0.03933.