This paper presents a real-time application of see-through technology using computer vision (e.g., object detection) and Vehicle-to-X (V2X) communication (e.g., Vehicle-to-Vehicle (V2) and Vehicle-to-Infrastructure (V2I)). Our wireless communication testbed uses three access points (APs) mounted approximately 3.3 meters from the ground along a road on our campus. Each AP was connected to Chattanooga’s fiber optics internet, supporting a data transfer rate up to 10-Gpbs. Using a 5Ghz frequency, we set up vehicular communications with a seamless handover for transferring real-time data. Two web cameras acting as clients were mounted on the windshield of two of three vehicles. Each client captured and sent 30 frames per second to our server. Using multi-threaded programming, we processed both image feeds simultaneously, which is exponentially quicker than single-thread image transferring/processing. To further increase processing efficiency, the server analyzed every tenth frame it received from the image feed, resulting in a processing latency decrease of 90%. Once the server received the images, it performed an object recognition algorithm on each image using a convolutional neural network (CNN). Post-identification, the images from the second vehicle were sent and overlaid dynamically to the third vehicle’s image. This repetitive overlapping of images allowed the third vehicle to “see-through” the second vehicle in real-time. We showcased our application during the US Ignite Smart Cities Summit in June 2017 to emphasize the benefits of drivers being able to “see-through” the car in front to make more intelligent decisions when passing a vehicle, stopping for a pedestrian, or seeing an upcoming detour due to construction before the view is within their line of sight. In summary, using V2X communication with computer vision gives the driver a higher level of awareness and allows better decision making in the case of a roadway conflict, ultimately increasing the level of safety on our roadways.