🤖 AI Summary
This work addresses the challenge of deploying lightweight models for agricultural weed detection on resource-constrained edge devices, where performance gains through conventional model scaling are infeasible. To overcome this limitation, the authors propose a Model-Driven Data Correction (MDDC) framework that operates with a fixed YOLOv8n architecture. By conducting automated error analysis, detection failures are systematically categorized into four distinct types. This classification informs a structured iterative pipeline of training, data correction, and retraining, augmented by versioned data management to ensure traceable and systematic improvements in data quality. Notably, MDDC represents the first approach to integrate error-type taxonomy with iterative data refinement, effectively breaking through performance bottlenecks imposed by model constraints. Experiments across multiple weed datasets demonstrate consistent improvements, achieving mAP@0.5 gains of 5% to 25%.
📝 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.