针对鱼眼相机畸变问题，提出统一检测流水线提升交通监控准确性
A Unified Detection Pipeline for Robust Object Detection in Fisheye-Based Traffic Surveillance
- 通过预处理与后处理增强图像边缘区域的检测一致性
- 在AI City挑战赛中实现0.6366的F1分数，排名第八
- 适合需要高鲁棒性广角监控的智慧交通场景
鱼眼相机可通过单一视角捕捉大范围交通场景，但其强烈的径向畸变和非均匀分辨率给标准目标检测器带来挑战，尤其在图像边界处物体外观严重失真。本文提出一种针对该问题的检测框架，采用简单有效的预处理与后处理流水线，提升图像各区域检测的一致性。我们在鱼眼交通影像上训练多个前沿检测模型，并通过集成策略融合输出以提升整体精度。该方法在2025年AI City Challenge Track 4中取得0.6366的F1分数，62支队伍中排名第8。结果表明，该框架能有效应对鱼眼图像固有缺陷。
原文摘要 · Abstract (English)
Fisheye cameras offer an efficient solution for wide-area traffic surveillance by capturing large fields of view from a single vantage point. However, the strong radial distortion and nonuniform resolution inherent in fisheye imagery introduce substantial challenges for standard object detectors, particularly near image boundaries where object appearance is severely degraded. In this work, we present a detection framework designed to operate robustly under these conditions. Our approach employs a simple yet effective pre and post processing pipeline that enhances detection consistency across the image, especially in regions affected by severe distortion. We train several state-of-the-art detection models on the fisheye traffic imagery and combine their outputs through an ensemble strategy to improve overall detection accuracy. Our method achieves an F1 score of0.6366 on the 2025 AI City Challenge Track 4, placing 8thoverall out of 62 teams. These results demonstrate the effectiveness of our framework in addressing issues inherent to fisheye imagery.
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