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Multi-attribute object detection benchmark for smart city

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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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Acknowledgments

This work is partially supported by Natural Science Foundation of China under contract No. U19B2036, and Peng Cheng Laboratory Research Project No. PCL2021A07.

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Authors and Affiliations

  1. Peng Cheng Laboratory, Shenzhen, China

    Yaowei Wang & Zhouxin Yang

  2. College of Intelligence and Computing, Tianjin University, Tianjin, China

    Rui Liu, Deng Li, Yuandu Lai & Yahong Han

  3. College of Electrical and Information Engineering, Hunan University, Changsha, China

    Lihan Ouyang & Leyuan Fang

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  1. Yaowei Wang
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  2. Zhouxin Yang
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  3. Rui Liu
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  4. Deng Li
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  5. Yuandu Lai
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  6. Lihan Ouyang
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  7. Leyuan Fang
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  8. Yahong Han
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Correspondence to Yahong Han.

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Wang, Y., Yang, Z., Liu, R. et al. Multi-attribute object detection benchmark for smart city. Multimedia Systems 28, 2423–2435 (2022). https://doi.org/10.1007/s00530-022-00971-1

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