# WDS-YOLO: A Marine Benthos Detection Model Fusing Wavelet Convolution and Deformable Attention
## 1 Introduction
[24] developed a two-stage detection network based on Deformable Convolution Pyramid (DCP), which uses deformable convolution to adaptively match target deformations and occlusion features, combined with a phased learning strategy to enhance multi-domain generalization capability. [PERSON] et al. [25] embedded an adaptive convolutional kernel selection unit and a deconvolutional feature pyramid into the Faster R-CNN framework, effectively addressing the missed detection of small underwater targets. [PERSON] et al. [26] integrated the Transformer mechanism into YOLOv4, enhancing feature discrimination in complex underwater backgrounds through self-attention mechanisms. [PERSON] et al. [27] designed a lightweight feature fusion module based on MobileNet v2, optimizing model deployment efficiency on low-computation devices. [PERSON] et al. [28] enhanced YOLOv5 by incorporating the CBAM and CRFPN networks, which improved the feature representation of blurred underwater targets. [PERSON] et al. [29] enhanced shallow feature extraction in YOLOv5s through the synergistic use of Coordinate Attention (CA) and Squeeze-and-Excitation (SE) modules. [PERSON] et al. [30] combined EfficientNetV2-S with Bottleneck Transformer to optimize a lightweight YOLOv5, balancing detection accuracy and computational cost. [PERSON] et al. [31] improved the FPN structure and integrated the SENet mechanism with YOLOv7, strengthening the multi-scale feature association of small underwater objects. [PERSON] et al. [32] used the RTMDet backbone network and BoT3 module to enhance the context modeling capability of YOLOv5. [PERSON] et al. [33] proposed the TC-YOLO model, which uses Transformer and Coordinate Attention to collaboratively model the spatial relationships of underwater objects. [PERSON] et al. [34] designed lightweight ODConv and GSConv modules based on YOLOv6, reducing the computational complexity of complex underwater scenarios. [PERSON] et al. [35] improved the YOLOv7 network architecture by optimizing the E-ELAN structure with the ACmixBlock module and skip connections, and introduced the Global Attention Mechanism (GAM) to enhance target discrimination in complex underwater environments. [PERSON] et al. [36] constructed Cross-Stage Multi-Branch (CSMB) and Large Kernel Spatial Pyramid (LKSP) modules in YOLOv8, improving multi-scale underwater object detection performance. [PERSON] et al. [37] used FasterNet to optimize the feature pyramid of YOLOv8, achieving high-frame-rate real-time underwater detection. [PERSON] et al. [38] designed the LEPC module and AP-FasterNet architecture, which preserves small object spatial details through Content-Aware ReAssembly (CARAFE) up-sampling. [PERSON] et al. [39] improved YOLOv9s feature fusion based on the Dual Dynamic Token Mixer (D-Mixer), enhancing feature consistency for dynamic blurry objects. [PERSON] et al. [40] developed an enhanced YOLOX framework by integrating MobileViT and Dual Coordinate Attention (DCA), which enhances global feature extraction efficiency through a lightweight ViT backbone network and strengthens shallow feature representation with the DCA mechanism, compressing model parameters by 49.6% while maintaining detection accuracy, significantly meeting the low-computation requirements of underwater unmanned platforms. To address these issues, this paper proposes a marine benthos object detection algorithm, WDS-YOLO, based on YOLOv8, with three main contributions:  
* WTConv was used to expand the receptive field, enhancing the model's ability to extract and represent features in complex underwater environments. This capability is critical for robotic systems to accurately locate partially buried scallops and starfish in sandy substrates, contributing to enhanced fishing efficiency. * A Deformable Attention-based Spatial Pyramid Pooling Fast (DASPPF) module was designed, which dynamically adjusts the network's attention to objects, reducing interference from complex backgrounds in detection tasks.