YOLO-BEV: Generating Bird's-Eye View in the Same Way as 2D Object
  Detection
Chang Liu Liguo Zhou Yanliang Huang Alois Knoll
Vehicle perception systems strive to achieve comprehensive and rapid visual
interpretation of their surroundings for improved safety and navigation. We
introduce YOLO-BEV, an efficient framework that harnesses a unique surrounding
cameras setup to generate a 2D bird's-eye view of the vehicular environment. By
strategically positioning eight cameras, each at a 45-degree interval, our
system captures and integrates imagery into a coherent 3x3 grid format, leaving
the center blank, providing an enriched spatial representation that facilitates
efficient processing. In our approach, we employ YOLO's detection mechanism,
favoring its inherent advantages of swift response and compact model structure.
Instead of leveraging the conventional YOLO detection head, we augment it with
a custom-designed detection head, translating the panoramically captured data
into a unified bird's-eye view map of ego car. Preliminary results validate the
feasibility of YOLO-BEV in real-time vehicular perception tasks. With its
streamlined architecture and potential for rapid deployment due to minimized
parameters, YOLO-BEV poses as a promising tool that may reshape future
perspectives in autonomous driving systems.