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Object detection is an algorithm that recognizes and locates the objects inthe image and has a wide range of applications in the visual understanding ofcomplex urban scenes. Existing object detection benchmarks mainly focus on asingle specific scenario and their annotation attributes are not rich enough,these make the object detection model is not generalized for the smart cityscenes. Considering the diversity and complexity of scenes in intelligent citygovernance, we build a large-scale object detection benchmark for the smartcity. Our benchmark contains about 500K images and includes three scenarios:intelligent transportation, intelligent security, and drones. For thecomplexity of the real scene in the smart city, the diversity of weather,occlusion, and other complex environment diversity attributes of the images inthe three scenes are annotated. The characteristics of the benchmark areanalyzed and extensive experiments of the current state-of-the-art targetdetection algorithm are conducted based on our benchmark to show theirperformance.

Peng Cheng Object Detection Benchmark for Smart City