Intelligent Transportation System (ITS) combines a transportation system with an Information and Communication Technology (ICT) system, where the ICT system plays a role in adding functionality in the form of intelligence resembling human intelligence to the transportation system. The combination allows humans to know the real state of the transportation system including transportation components, such as the status of the road, objects around the vehicle, and the state of the vehicle, thus enabling humans to optimize the transportation system. For example, if there is a group of thief that using a van on the road, the process of detecting where is the route used by the thief can be faster by adding a vehicle detector to the traffic light camera. This detector will work better if the detector can detect the van in real-time and in a high-resolution image. This thesis will develop a detector system that can process a high-resolution image but still maintain the inference time (fps) high using HRNet and FCOS. HRNet is a high-resolution image network architecture that can process the image in multiple resolutions (low, medium, high) to maintain the high resolution but still have enough image feature to process, while FCOS is a one-stage anchor-free detector, so it can detect the object faster than the anchor-based detector. Our experimental results show that our system has a better result compared with the reference result using the same dataset and hyperparameter. It also has a better result compared with the reference result that uses the reference dataset and hyperparameter.