Confident Learning for Object Detection under Model Constraints
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866909993122398208 |
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| author | Yu, Yingda Xuan, Jiaqi Shi, Shuhui Teng, Xuanyu Xu, Shuyang Tong, Guanchao |
| author_facet | Yu, Yingda Xuan, Jiaqi Shi, Shuhui Teng, Xuanyu Xu, Shuyang Tong, Guanchao |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_11640 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Confident Learning for Object Detection under Model Constraints Yu, Yingda Xuan, Jiaqi Shi, Shuhui Teng, Xuanyu Xu, Shuyang Tong, Guanchao Computer Vision and Pattern Recognition 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. |
| title | Confident Learning for Object Detection under Model Constraints |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2601.11640 |