An Object Detection Method Based on Confidence-Stratified Collaborative Decision-Making

JIN Miaomiao; YU Xuyi; ZHAO Yinghai; ZHANG Huipeng; SHANG Yinghui; MEI Kuizhi

doi:10.7652/xjtuxb202610002

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An Object Detection Method Based on Confidence-Stratified Collaborative Decision-Making

Special Topic Fusion Perception and Decision-Making Planning for Intelligent Driving | 更新时间：2026-09-29

- An Object Detection Method Based on Confidence-Stratified Collaborative Decision-Making
- Journal of Xi'an Jiaotong University Vol. 60, Issue 10, Pages: 22-33(2026)
- 作者机构：
  
  西安交通大学人机混合增强智能全国重点实验室，710049，西安
  西安交通大学人工智能与机器人研究所，710049，西安
  北京华航无线电测量研究所，100013，北京
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb202610002
  CLC： TP399
- Received：26 February 2026，
  
  Online First：11 May 2026，
  
  Published：10 October 2026
- 稿件说明：
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JIN Miaomiao, YU Xuyi, ZHAO Yinghai, et al. An Object Detection Method Based on Confidence-Stratified Collaborative Decision-Making[J/OL]. Journal of Xi'an Jiaotong University,2026,60 (10):22-33. https://doi.org/10.7652/xjtuxb202610002.
DOI：

JIN Miaomiao, YU Xuyi, ZHAO Yinghai, et al. An Object Detection Method Based on Confidence-Stratified Collaborative Decision-Making[J/OL]. Journal of Xi'an Jiaotong University,2026,60 (10):22-33. https://doi.org/10.7652/xjtuxb202610002. DOI：

摘要

针对实时目标检测模型置信度校准不足以及后处理决策机制单一所引发的误检与漏检问题，提出了一种置信度分层协同决策架构。该架构以YOLOv5模型为基础检测网络，在推理阶段构建置信度分层机制，根据预测置信度分布，将候选框划分为高置信度直通路径和低置信度精细化判别路径。与此同时，引入极端梯度提升（XGBoost）模型，对低置信度样本进行二次分类与置信度修正，实现检测结果的差异化处理与计算资源的分配。在融合阶段设计统一的决策整合模块，对不同路径输出进行结构化融合与筛选，在不改变主干网络结构和基本不增加计算复杂度的前提下，提升了整体检测性能与预测可靠性。研究结果表明：在2080Ti测试环境下、COCO数据集上，改进后YOLOv5s和YOLOv5m模型的平均精度均值

AP，50-95

分别提升了1.98%、1.99%；泛化至YOLOv8m时，模型的

AP，50-95

为55.07%，推理速度为165.4帧/s。该方法构建了深度神经网络与传统机器学习模型协同决策的新型融合范式，为高可靠领域目标识别系统提供了一种兼顾精度、效率与可信度的技术路径。

Abstract

To address the issues of false positives and false negatives caused by insufficient confidence calibration and single post-processing decision mechanisms in real-time object detection models

a confidence-stratified collaborative decision-making framework is p

roposed. Using the YOLOv5model as the base detection network

the framework constructs a confidence stratification mechanism during the inference stage. Based on the predicted confidence distribution

candidate bounding boxes are divided into a high-confidence passthrough path and a low-confidence refined discrimination path. Meanwhile

an extreme gradient boosting (XGBoost) model is introduced to perform secondary classification and confidence recalibration on low-confidence samples

achieving differentiated processing of detection results and optimized allocation of computational resources. In the fusion stage

a unified decision integration module is designed to perform structured fusion and filtering on the outputs from different paths. This improves overall detection performance and predictive reliability without altering the backbone network structure or significantly increasing computational complexity. Experimental results show that

validated on the COCO dataset in a 2080Ti testing environment

the mean average precision

50-95

of the improved YOLOv5s and YOLOv5m models increased by 1.98% and 1.99%

respectively. When generalized to YOLOv8m

the model achieved an

50-95

of 55.07% with an inference speed of 165.4frame/s. This method establishes a novel fusion paradigm for collaborative decision-making between deep neural networks and traditional machine learning models

providing a technical pathway that balances accuracy

efficiency

and credibility for object recognition systems in high-reliability domains.

关键词

Keywords

references

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