基于固定闭路电视（CCTV）摄像头的鲁棒车辆检测对智能交通系统（ITS）至关重要。然而，现有基准数据集主要涵盖相对同质化、高度有序的交通场景，且多采集自以车为中心的驾驶视角或受控的航拍视角。这种地域性与传感器视角偏差造成了显著差距：在UA-DETRAC和COCO等数据集上训练的模型难以泛化至新兴经济体中快速发展的城市中心所呈现的高密度、异质化、无序化的交通状况。为弥补这一局限，我们提出BMD-45——一个大规模数据集，包含来自3600余台实际运行的“平安城市”CCTV摄像头采集的45,000张图像，共标注480,000个边界框。BMD-45涵盖14类细粒度车辆类别，包括现有基准未覆盖的区域性交通工具，如三轮自动人力车（auto-rickshaws）和小型客运面包车（tempo travellers）。该数据集真实反映部署挑战，涵盖极端视角变化、遮挡及高车辆密度等情形。我们采用当前最先进的检测器建立了全面基线，并揭示出显著的领域差异：在UA-DETRAC上微调的模型在BMD-45上的[email protected]:0.95仅为33.6%，而直接在BMD-45上训练时达83.8%，性能提升达2.5倍；该差距即使在计入新型车辆类别后依然存在。这一性能落差凸显了构建地理多样性交通基准的迫切需求，并确立BMD-45作为全球代表性不足城市环境中鲁棒感知系统开发的基准数据集。数据集地址：https://huggingface.co/datasets/iisc-aim/BMD-45。

Robust vehicle detection from fixed CCTV cameras is critical for Intelligent Transportation Systems. Yet existing benchmarks predominantly feature relatively homogeneous, highly organized traffic patterns captured from ego-centric driving perspectives or controlled aerial views. This regional and sensor view bias creates a significant gap. Models trained on datasets such as UA-DETRAC and COCO struggle to generalize to the dense, heterogeneous, disorganized traffic conditions observed in rapidly developing urban centers in emerging economies. To address this limitation, we introduce BMD-45, a large-scale dataset comprising 480K bounding boxes annotated over 45K images captured from over 3.6K operational Safe City CCTV cameras. BMD-45 contains 14 fine-grained vehicle categories, including region-specific modes such as auto-rickshaws and tempo travellers, which are not present in existing benchmarks. The dataset captures real-world deployment challenges, including extreme viewpoint variation, occlusion, and vehicle density . We establish comprehensive baselines using state-of-the-art detectors and reveal a striking domain gap: models fine-tuned on UA-DETRAC achieve only 33.6% [email protected]:0.95, compared to 83.8% when trained in-domain on BMD-45, representing a 2.5x improvement that persists even when accounting for novel vehicle classes. This performance gap underscores the critical need for geographically diverse traffic benchmarks and establishes BMD-45 as a baseline for developing robust perception systems in underrepresented urban environments worldwide. The dataset is available at: https://huggingface.co/datasets/iisc-aim/BMD-45.