The proliferation of IoT and edge computing devices demands for efficient deployment strategies due to their limited computational capabilities and energy constraints. This paper investigates the application of deep learning models in such resource-constrained environments, focusing on the quantization of the MobileNetV2 architecture. We evaluate three primary quantization techniques: dynamic quantization, static quantization, and quantization-aware training. Using a subset of the Vehicle Make and Model Recognition dataset, specifically the Most Stolen Vehicles in the US in 2017, we compare our results with a previous study, highlighting the advancements achieved through MobileNetV2 and its quantization process. Deployment on two STM32 boards, L4 and H7 series, demonstrates the effectiveness of the quantized MobileNetV2 model in achieving efficient, low-power, and low-latency execution on low-power MCUs.