Correction: Li et al. FSDN-DETR: Enhancing Fuzzy Systems Adapter with DeNoising Anchor Boxes for Transfer Learning in Small Object Detection. Mathematics 2025, 13, 287

Zhijie Li; Jiahui Zhang; Yingjie Zhang; Dawei Yan; Xing Zhang; Marcin Woźniak; Wei Dong

doi:10.3390/math13213530

,

and

¹

College of Information and Control Engineering, Xi’an University of Architecture and Technology, Xi’an 710055, China

²

Institute of Mathematics, Silesian University of Technology, Kaszubska 23, 44-100 Gliwice, Poland

^*

Authors to whom correspondence should be addressed.

Mathematics2025, 13(21), 3530;https://doi.org/10.3390/math13213530

This article belongs to the Special Issue Image Processing and Machine Learning with Applications

Version Notes

Order Reprints

Error in Table

In the original publication [1], there was a mistake in Table 3. During the final submission, the file for Table 3 was inadvertently overwritten, causing Table 3 to duplicate the contents of Table 2 (COCO results). The corrected Table 3 (AI-TOD-V2 results: (APvt), (APt), (APs), and (APm)) appears below. The authors state that the scientific conclusions are unaffected. This correction was approved by the Academic Editor. The original publication has also been updated.

Table 3. Performance comparison of Fuzzy-DETR and baseline models on the AI-TOD-V2 test dataset, evaluated using AP metrics for very tiny (

A P_{v t}

), tiny (

A P_{t}

), small (

A P_{s}

), and medium (

A P_{m}

) object sizes.

Text Correnction

There was an error in the original publication. One data point was redundantly described. A correction has been made to Section 4, Sub-section 4.6, Paragraph 4:

In addition to small and very small objects, FSDN-DETR demonstrates strong performance across medium objects, with AP_m scores of 46.91, respectively.

The authors state that the scientific conclusions are unaffected. This correction was approved by the Academic Editor. The original publication has also been updated.

Reference

Li, Z.; Zhang, J.; Zhang, Y.; Yan, D.; Zhang, X.; Woźniak, M.; Dong, W. FSDN-DETR: Enhancing Fuzzy Systems Adapter with DeNoising Anchor Boxes for Transfer Learning in Small Object Detection. Mathematics 2025, 13, 287. [Google Scholar] [CrossRef]

Table 3. Performance comparison of Fuzzy-DETR and baseline models on the AI-TOD-V2 test dataset, evaluated using AP metrics for very tiny (

A P_{v t}

), tiny (

A P_{t}

), small (

A P_{s}

), and medium (

A P_{m}

) object sizes.

Table 3. Performance comparison of Fuzzy-DETR and baseline models on the AI-TOD-V2 test dataset, evaluated using AP metrics for very tiny (

A P_{v t}

), tiny (

A P_{t}

), small (

A P_{s}

), and medium (

A P_{m}

) object sizes.

Basemodel	Method	Backbone	AP	${AP}_{vt}$	${AP}_{t}$	${AP}_{s}$	${AP}_{m}$
CNN-based	RetinaNet [25]	ResNet50-FPN	8.92	2.49	8.36	13.06	19.89
	Mask R-CNN [56]	ResNet101-FPN	9.83	1.84	8.76	18.53	23.70
	Faster R-CNN [21]	ResNet101-FPN	13.65	0.04	9.53	25.71	37.84
DETR-like	Deformable DETR [5]	ResNet50	18.86	6.49	17.45	25.03	34.14
	DN-DETR [7]	ResNet50	23.38	9.86	22.60	29.74	38.81
	DINO-DETR [8]	ResNet50	25.88	13.02	25.09	32.02	39.65
	DQ-DETR [12]	ResNet50	29.73	15.09	29.38	36.38	44.64
Ours	Fuzzy-DETR	ResNet50	31.58	18.20	31.51	37.43	46.91

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Correction: Li et al. FSDN-DETR: Enhancing Fuzzy Systems Adapter with DeNoising Anchor Boxes for Transfer Learning in Small Object Detection. Mathematics 2025, 13, 287

Error in Table

Text Correnction

Reference

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