YOLO-FWF: a lightweight frequency–wavelet feature fusion network for robust scene text detection
Year: 2026
Type: article
Abstract: Scene text detection remains challenging due to large scale variations, slender stroke structures, complex backgrounds, low contrast, and arbitrary text orientations. To address these issues, this paper proposes YOLO-FWF, an improved lightweight scene text detection model based on YOLO11n. First, a C3k2_FrequencyCM module is introduced into the backbone to enhance fine-grained text representation by combining Fourier-domain amplitude compensation with spatial high-frequency feature enhancement, thereby improving the sensitivity of the network to text strokes, edges, and weak texture cues. Second, a C2PSA_WDAM module is designed to replace the original C2PSA structure. By decomposing features into low-frequency semantic components and high-frequency detail components through wavelet transform, the module uses high-frequency information to guide attention modeling and suppress background interference. Third, a Fourier Angle Alignment Fusion module is proposed for multi-scale feature fusion. It estimates dominant local orientations in the frequency domain and aligns high-level semantic features with low-level structural features before fusion, improving the detection of inclined, curved, and irregular text instances. Experiments on two Roboflow-hosted scene text datasets demonstrate that YOLO-FWF achieves superior detection accuracy while maintaining a lightweight structure. On the text scene computer vision dataset, YOLO-FWF reaches 86.8% mAP50 and 64.2% mAP50:95 with only 2.89 M parameters and 6.7 GFLOPs. On the text detection dataset, it achieves 54.7% mAP50 and 34.6% mAP50:95, outperforming YOLO11n and other mainstream detectors under the same evaluation setting. These results verify the effectiveness of frequency-domain enhancement, wavelet-guided attention, and direction-aware feature fusion on the evaluated datasets. Because the current experiments do not include standard public scene-text benchmarks, broader generalization remains to be further verified.
Source: Scientific Reports
Institution: Hebei University of Architecture
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,690
OpenAlex ID: W7223188050