| Title | Multi-attribute object detection benchmark for smart city |
|---|---|
| ID_Doc | 37340 |
| Authors | Wang, YW; Yang, ZX; Liu, R; Li, D; Lai, YD; Ouyang, LH; Fang, LY; Han, YH |
| Title | Multi-attribute object detection benchmark for smart city |
| Year | 2022 |
| Published | Multimedia Systems, 28.0, 6 |
| Abstract | 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 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 100K images and includes three scenarios: intelligent transportation, intelligent surveillance, and drone. 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. Our benchmark is available at https://openi.org.cn/projects/Benchmark. |
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