Confident Learning for Object Detection under Model Constraints

• URL: http://arxiv.org/abs/2601.11640v1
• Date: Wed, 14 Jan 2026 15:32:08 GMT
• Title: Confident Learning for Object Detection under Model Constraints
• Authors: Yingda Yu, Jiaqi Xuan, Shuhui Shi, Xuanyu Teng, Shuyang Xu, Guanchao Tong,
• Abstract summary: Agricultural weed detection on edge devices is subject to strict constraints on model capacity, computational resources, and real-time inference latency.<n>This paper proposes Model-Driven Data Correction (MDDC), a data-centric framework that enhances detection performance by iteratively diagnosing and correcting data quality deficiencies.
• Score: 0.05131152350448099
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Agricultural weed detection on edge devices is subject to strict constraints on model capacity, computational resources, and real-time inference latency, which prevent performance improvements through model scaling or ensembling. This paper proposes Model-Driven Data Correction (MDDC), a data-centric framework that enhances detection performance by iteratively diagnosing and correcting data quality deficiencies. An automated error analysis procedure categorizes detection failures into four types: false negatives, false positives, class confusion, and localization errors. These error patterns are systematically addressed through a structured train-fix-retrain pipeline with version-controlled data management. Experimental results on multiple weed detection datasets demonstrate consistent improvements of 5-25 percent in mAP at 0.5 using a fixed lightweight detector (YOLOv8n), indicating that systematic data quality optimization can effectively alleviate performance bottlenecks under fixed model capacity constraints.

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