Peng Cheng Object Detection Benchmark for Smart City

TL;DR

This paper introduces a large-scale object detection benchmark tailored for smart city applications, covering diverse urban scenarios with rich annotations to improve model generalization in complex environments.

Contribution

The creation of a comprehensive, multi-scenario benchmark with extensive annotations for object detection in smart city scenes is a novel contribution.

Findings

State-of-the-art algorithms show varied performance across scenarios.

Rich annotations help in understanding model robustness in complex environments.

Benchmark facilitates future research in urban scene object detection.

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 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.