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.

翻译：车辆感知系统致力于实现对其周围环境全面且快速的视觉解读，以提升安全性与导航能力。我们提出YOLO-BEV，一种高效框架，利用独特的环境相机设置来生成车辆环境的二维鸟瞰视图。通过策略性布置八个间隔45度的相机，我们的系统捕捉并整合图像，形成连贯的3×3网格格式（中心留空），从而提供丰富的空间表征以促进高效处理。在该方法中，我们采用YOLO的检测机制，发挥其快速响应与紧凑模型结构的固有优势。不同于传统YOLO检测头，我们通过定制设计的检测头对其进行增强，将全景捕获数据转化为自车的统一鸟瞰视图地图。初步结果验证了YOLO-BEV在实时车辆感知任务中的可行性。凭借其流线型架构及因参数最小化而具备的快速部署潜力，YOLO-BEV有望成为重塑自动驾驶系统未来视角的重要工具。