SFFNet: Synergistic Feature Fusion Network With Dual-Domain Edge Enhancement for UAV Image Object Detection

Wenfeng Zhang; Jun Ni; Yue Meng; Xiaodong Pei; Wei Hu; Qibing Qin; Lei Huang

cs.CVApr 3, 2026

SFFNet: Synergistic Feature Fusion Network With Dual-Domain Edge Enhancement for UAV Image Object Detection

Authors: Wenfeng Zhang, Jun Ni, Yue Meng, Xiaodong Pei, Wei Hu, Qibing Qin, Lei Huang

Organizations: College of Computer and Information Science, Chongqing Normal University, Chongqing 401333, China · CETC Yizhihang (Chongqing) Technology Co., Ltd, Chongqing 400031, China · School of Computer Engineering, Weifang University, Weifang 261061, China · Faculty of Information Science and Engineering, Ocean University of China, Qingdao 266005, China

Abstract

Object detection in unmanned aerial vehicle (UAV) images remains a highly challenging task, primarily caused by the complexity of background noise and the imbalance of target scales. Traditional methods easily struggle to effectively separate objects from intricate backgrounds and fail to fully leverage the rich multi-scale information contained within images. To address these issues, we have developed a synergistic feature fusion network (SFFNet) with dual-domain edge enhancement specifically tailored for object detection in UAV images. Firstly, the multi-scale dynamic dual-domain coupling (MDDC) module is designed. This component introduces a dual-driven edge extraction architecture that operates in both the frequency and spatial domains, enabling effective decoupling of multi-scale object edges from background noise. Secondly, to further enhance the representation capability of the model's neck in terms of both geometric and semantic information, a synergistic feature pyramid network (SFPN) is proposed. SFPN leverages linear deformable convolutions to adaptively capture irregular object shapes and establishes long-range contextual associations around targets through the designed wide-area perception module (WPM). Moreover, to adapt to the various applications or resource-constrained scenarios, six detectors of different scales (N/S/M/B/L/X) are designed. Experiments on two challenging aerial datasets (VisDrone and UAVDT) demonstrate the outstanding performance of SFFNet-X, achieving 36.8 AP and 20.6 AP, respectively. The lightweight models (N/S) also maintain a balance between detection accuracy and parameter efficiency. The code will be available at https://github.com/CQNU-ZhangLab/SFFNet.

Figures & tables

Fig. 1: The relationship between the AP value and the number of parameters for different object detection algorithms on the VisDrone dataset. The points near the upper left corner of the figure indicate that the model can achieve higher accuracy while maintaining a lower number of parameters. Our algorithm (marked “Ours”) outperforms others by achieving a higher AP value with fewer parameters.

Fig. 2: Overview of the SFFNet framework. The framework integrates a backbone network with MDDC for efficient multi-scale dual-domain feature extraction, and realizes collaborative feature fusion in the neck through SFPN. This design ensures the accurate positioning and detection of small objects, effectively addressing the challenges in complex environments.

Fig. 3: The detailed structure of the MDDC module. The MDDC module initially performs multi-scale decomposition on the input feature map to construct the base representation. Subsequently, through DEIE, it achieves complementary feature fusion in both the spatial and frequency domains. In the frequency domain branch of DEIE, features undergo spectral decomposition, removing low-frequency noise disturbances while enhancing high-frequency feature representations.

Fig. 4: The detailed structure of the WPM. WPM employs a parallel structure consisting of a large kernel convolution, a small kernel convolution, and two strip convolutions.

Model	$\mathrm{AP}$	$\mathrm{AP}_{\mathrm{50}}$	$\mathrm{AP}_{\mathrm{75}}$	$\mathrm{AP}_{\mathrm{s}}$	$\mathrm{AP}_{\mathrm{m}}$	$\mathrm{AP}_{\mathrm{l}}$	$\mathrm{AR}_{\mathrm{1}}$	$\mathrm{AR}_{\mathrm{10}}$	$\mathrm{AR}_{\mathrm{100}}$	$\mathrm{AR}_{\mathrm{s}}$	$\mathrm{AR}_{\mathrm{m}}$	$\mathrm{AR}_{\mathrm{l}}$	Param(M)	FLOPs(G)
YOLOv8-N † [ 33 ]	23.7	40.2	24.0	16.8	32.0	34.5	9.9	28.9	38.8	31.5	49.3	48.9	3.2	8.7
YOLOv10-N † [ 34 ]	22.9	39.9	22.8	17.1	30.6	26.0	9.3	28.1	38.7	32.0	48.1	42.2	2.3	6.7
YOLO11-N † [ 35 ]	23.9	40.6	23.9	16.9	32.5	33.3	9.6	29.0	38.9	31.1	50.1	48.9	2.6	6.5
SFFNet-N (Ours)	26.7	45.4	26.8	19.8	35.3	35.0	10.4	31.5	42.6	35.6	52.8	52.1	1.7	7.2
YOLOv8-S † [ 33 ]	27.8	46.6	28.2	20.6	37.0	35.8	11.1	32.2	42.6	35.4	53.1	48.2	11.2	28.6
YOLOv10-S † [ 34 ]	27.2	46.2	27.5	20.8	35.6	31.9	10.7	31.9	43.2	36.7	52.5	46.5	7.2	21.6

TABLE I: Comparison of Our Model with Several YOLO Detectors on the VisDrone Validation Set. The Best Result for Each Scale Is Marked in Bold, and † Denotes a Reimplementation of the Results under the Same Experimental Setting

Model	Venue	$\mathrm{AP}$	$\mathrm{AP}_{\mathrm{50}}$	$\mathrm{AP}_{\mathrm{75}}$	$\mathrm{AP}_{\mathrm{s}}$	$\mathrm{AP}_{\mathrm{m}}$	$\mathrm{AP}_{\mathrm{l}}$	Param(M)	FLOPs(G)
Faster-RCNN † [ 41 ]	NeurIPS 2015	24.7	40.5	26.2	15.7	36.9	37.9	41.4	208.0
ClusDet [ 14 ]	ICCV 2019	28.4	53.2	26.4	19.1	40.8	54.4	-	-
DMNet [ 15 ]	CVPRW 2020	29.4	49.3	30.6	21.6	41.0	56.9	-	-
GFL † [ 42 ]	NeurIPS 2020	26.4	42.7	27.7	17.4	38.3	45.6	52.3	283.0
AMRNet [ 43 ]	ArXiv 2020	32.1	-	-	23.2	43.9	60.5	-	-
TOOD † [ 44 ]	ICCV 2021	27.5	44.0	28.8	18.6	39.1	43.2	32.0	199.0

TABLE II: Comparison of Other State-of-the-Art Models with Our Models on the Validation Set of the VisDrone Dataset. The Red Font Represents the Best Result Achieved for This Indicator, the Green Font Represents the Second-Best Result, and the Gray Font Represents the Third-Best Result. “-” Indicates That the Corresponding Value Is Not Reported in the Relevant Paper. † Denotes a Reimplementation of the Results under the Same Experimental Setting

Model	$\mathrm{AP}$	$\mathrm{AP}_{\mathrm{50}}$	$\mathrm{AP}_{\mathrm{75}}$	$\mathrm{AP}_{\mathrm{s}}$	$\mathrm{AP}_{\mathrm{m}}$	$\mathrm{AP}_{\mathrm{l}}$
ClusDet [ 14 ]	13.7	26.5	12.5	9.1	25.1	31.2
CenterNet [ 64 ]	13.2	26.7	11.8	7.8	26.6	13.9
DMNet [ 15 ]	14.7	24.6	16.3	9.3	26.2	35.2
GFL † [ 42 ]	17.0	30.3	18.1	11.2	28.5	30.9
AMRNet [ 43 ]	18.2	30.4	19.8	10.3	31.3	33.5
GLSAN [ 46 ]	19.0	30.5	21.7	-	-	-

TABLE III: Comparison of Other State-of-the-Art Models with Our Models on the UAVDT Dataset. The Best Result Is Marked in Bold. “-” Indicates That the Corresponding Value Is Not Reported in the Relevant Paper. † Denotes a Reimplementation of the Results under the Same Experimental Setting

	MDDC		SFPN		AP	AP 50	AP 75	AP s	AP m	AP l	Param(M)	FLOPs(G)
	DEIE	w/o DEIE	LDConv	WPM	AP	AP 50	AP 75	AP s	AP m	AP l	Param(M)	FLOPs(G)
I					27.2	46.2	27.5	20.8	35.6	31.9	7.2	21.6
II		✓			27.8	47.1	28.2	20.9	37.1	32.1	7.4	20.0
III	✓	✓			28.1	47.6	28.4	21.3	37.2	32.5	7.4	20.1
IV				✓	29.4	49.6	29.9	22.9	38.4	31.3	5.8	22.1
V			✓	✓	31.3	52.0	31.8	24.6	40.3	37.1	6.1	25.4

TABLE IV: Ablation Accuracy Comparison between the Baseline Model and the Models of Different Components in Our Method on the VisDrone Dataset Validation Set, Where w/o DEIE Represents the MDDC Module without the DEIE Component

Fig. 5: The qualitative results of the ablation experiment for all fine-grained categories on the validation set of the VisDrone dataset. Deeper colors indicate higher AP values and better detection performance. ”+All” indicates the addition of all components.

Configuration	Frequency	Spatial	Edge strength map	AP	AP s	AP m	AP l
Baseline	$\times$	$\times$	$\times$	27.2	20.8	35.6	31.9
+ Frequency domain	✓	$\times$	$\times$	27.7	21.1	36.2	32.1
+ Spatial domain	$\times$	✓	$\times$	27.5	21.0	36.0	31.8
+ Dual domain	✓	✓	$\times$	27.9	21.2	36.8	32.3
+ All	✓	✓	✓	28.1	21.3	37.2	32.5

TABLE V: Detailed ablation study of DEIE components within the MDDC module

	WPM	AP	AP 50	AP 75	AP s	AP m	AP l	Param(M)	FLOPs(G)
I	$1{\times}1,\;1{\times}3,\;3{\times}1,\;3{\times}3$	31.3	52.1	32.2	25.0	41.1	34.0	6.2	22.6
II	$1{\times}1,\;1{\times}7,\;7{\times}1,\;7{\times}7$	31.1	51.8	32.1	24.8	40.7	34.9	6.2	22.6
III	$1{\times}1,\;1{\times}15,\;15{\times}1,\;15{\times}15$	31.4	52.4	32.2	25.0	40.8	35.3	6.2	22.9
IV	$1{\times}1,\;1{\times}31,\;31{\times}1,\;31{\times}31$	31.6	52.8	32.3	25.3	40.8	35.7	6.3	24.0
V	$1{\times}1,\;1{\times}63,\;63{\times}1,\;63{\times}63$	31.7	53.0	32.4	25.4	41.0	35.8	6.6	27.6

TABLE VI: Ablation Study of Different WPM Internal Convolution Kernel Configurations

Model	$\mathrm{AP}$	$\mathrm{AP}_{\mathrm{50}}$	$\mathrm{AP}_{\mathrm{75}}$	$\mathrm{AP}_{\mathrm{s}}$	$\mathrm{AP}_{\mathrm{m}}$	$\mathrm{AP}_{\mathrm{l}}$	$\mathrm{AR}_{\mathrm{1}}$	$\mathrm{AR}_{\mathrm{10}}$	$\mathrm{AR}_{\mathrm{100}}$	$\mathrm{AR}_{\mathrm{s}}$	$\mathrm{AR}_{\mathrm{m}}$	$\mathrm{AR}_{\mathrm{l}}$	Param(M)	FLOPs(G)
PAFPN [ 23 ]	27.2	46.2	27.5	20.8	35.6	31.9	10.7	31.9	43.2	36.7	52.5	46.5	7.2	21.6
BIFPN [ 10 ]	28.6	48.1	29.1	22.2	37.4	31.7	11.1	33.1	44.2	37.6	54.3	44.7	5.2	18.7
AFPN [ 25 ]	27.7	46.1	28.4	20.6	37.3	36.4	11.0	32.3	42.6	35.3	54.0	51.1	7.6	24.2
RepGFPN [ 68 ]	27.8	47.1	28.3	21.4	36.2	34.9	10.9	32.4	43.7	37.2	53.5	47.7	9.6	23.2
MAFPN [ 69 ]	29.1	48.9	29.5	22.2	38.4	35.2	11.2	33.5	44.7	37.5	55.1	47.1	10.3	26.8
SFPN (Ours)	31.3	52.0	31.8	24.6	40.3	37.1	12.0	35.1	46.7	40.3	56.4	53.7	6.1	25.4

TABLE VII: Comparison of the Performance of SFPN and Other Feature Pyramids Networks on the VisDrone Validation Set

Fig. 6: The comparison of detection visualization results between the baseline model and SFFNet. The first three rows are from the VisDrone dataset, and the last three are from the UAVDT dataset. Our model performs better in handling dense areas from various perspectives (rows 2–4), noise occlusion resulting from specific weather conditions (row 5), and blur caused by camera rotation (rows 1 and 6).

Fig. 7: The comparison of heatmap visualization results between the baseline model and SFFNet on the VisDrone dataset. Our model not only suppresses irrelevant background noise but also focuses more on detecting the target objects.

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Small object detection in Unmanned Aerial Vehicle (UAV) imagery remains challenging under adverse conditions, including complex weather, low illumination, and sensor noise. These challenges mainly stem from severe background clutter, fine-grained detail degradation, and suboptimal semantic-spatial feature fusion, which jointly hinder robust small-object representation. To this end, we propose FRFDet, a lightweight yet effective single-stage detector tailored for UAV-based small object detection. FRFDet proposes two plug-and-play modules: Inverse Bidirectional Sampling (IBS) and Scale-Feature Relationship Cross-Fusion (SFRCF). IBS preserves critical spatial details via channel expansion-compression and bidirectional pattern reconstruction, improving feature alignment. SFRCF explicitly models scale-dependent fusion behaviors, revealing that inter-group element-wise multiplication favors compact models, while inter-group additive fusion benefits larger architectures. Extensive experiments on VisDrone, UAVDT, HazyDet, and MS COCO demonstrate that FRFDet achieves state-of-the-art performance among lightweight detectors with low computational cost, compact parameters, and fast inference, making it well suited for resource-constrained UAV platforms.

Abstract

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