论文标题
彭昌对象检测基准的智能城市
Peng Cheng Object Detection Benchmark for Smart City
论文作者
论文摘要
对象检测是一种算法，可以在图像中识别并定位对象，并且在对复杂城市场景的视觉理解中具有广泛的应用。现有的对象检测基准主要关注单个特定方案，其注释属性不够丰富，这些使对象检测模型并未在智能城市场景中推广。考虑到智能城市治理中场景的多样性和复杂性，我们为智慧城市建立了一个大规模的对象检测基准。我们的基准分配约50万张图像，其中包括三种情况：智能运输，智能安全性和无人机。为了使智慧城市的真实场景的复杂性，在三个场景中的天气，遮挡和其他复杂的环境多样性属性的多样性被注释。分析了基准的特征，并根据我们的基准测试进行了最新目标检测算法的广泛实验，以显示其性能。
Object detection is an algorithm that recognizes and locates the objects in the image and has a wide range of applications in the visual understanding of complex urban scenes. Existing object detection benchmarks mainly focus on a single specific scenario and their annotation attributes are not rich enough, these make the object detection model is not generalized for the smart city scenes. Considering the diversity and complexity of scenes in intelligent city governance, we build a large-scale object detection benchmark for the smart city. Our benchmark contains about 500K images and includes three scenarios: intelligent transportation, intelligent security, and drones. For the complexity of the real scene in the smart city, the diversity of weather, occlusion, and other complex environment diversity attributes of the images in the three scenes are annotated. The characteristics of the benchmark are analyzed and extensive experiments of the current state-of-the-art target detection algorithm are conducted based on our benchmark to show their performance.
