Information about vehicles plays an important role in intelligent transportation systems (ITSs). It can be applied in different areas such as vehicle monitoring, vehicle detection, auxiliary reconnaissance, etc. Among the different types of vehicle-related information, logo information plays an essential role in quickly identifying the vehicle and enabling relevant work to be carried out. However, existing logo detection methods face issues related to low training accuracy and difficulty in accurately locating the logo, which leads to inaccurate detection of vehicle logos. To address these challenges, we first propose a method for detecting vehicle logos, particularly at expressway exits. We created a dataset comprising seven categories of vehicles for this purpose. Our solution includes a lightweight model that bridges CNN and transformer and a creative method for locating the logo. Additionally, we also do data processing on the test images to make them robust to environmental changes. The network that we designed is simple yet effective, achieving improvements in both precision and speed. Furthermore, our vehicle logo localization algorithm can withstand environmental variations. Experimental results demonstrate that our algorithm achieves a 5% to 10% accuracy boost compared with other methods.