# WDS-YOLO: A Marine Benthos Detection Model Fusing Wavelet Convolution and Deformable Attention
## 1 Introduction  
In recent years, the demand for nutrient-rich marine benthos, including holduurian, echinus, scallop and starfish, has surged, driving the rapid development of the aquaculture industry. However, current marine benthos fishing primarily relies on manual operations, which suffer from high costs, low efficiency, and significant safety risks [1]. In this large-scale industry, the use of underwater robots for automated fishing has emerged as a future trend [2, 3], offering cost reduction and enhanced safety and productivity, as well as marine ecosystem protection. A prerequisite for intelligent fishing technology is the accurate detection of marine benthos in complex underwater environments, where holdurians visually blend with sandy substrates, echinus densely aggregate with overlapping spines, scallops are partially buried under marine sediments, and starfish display diverse polymorphic features. These unique underwater complexities challenge conventional computer vision systems; thus, research on object detection methods for marine benthos holds significant research implications and practical application value. The field of object detection has progressed through three main paradigms: two-stage, one-stage, and transformer-based methods, each tackling unique challenges in accuracy, efficiency, and scalability. Two-stage detectors [4; 5], such as Faster R-CNN [6] and Cascade R-CNN [7], attain high precision by initially generating region proposals and subsequently refining them through classification and regression. However, their dependence on proposal generation and multi-stage processing leads to substantial computational overhead, restricting their applicability in real-time applications. Transformer-based methods, exemplified by RT-DETR [8], utilize self-attention mechanisms to model the global context and eliminate handcrafted components such as anchors. Despite their superior performance in complex scenarios, these models often demand extensive computational resources and large-scale training data, presenting challenges for deployment on resource-constrained devices. In contrast, one-stage detectors prioritize efficiency and simplicity, making them ideal for real-time applications. SSD [9] pioneered this paradigm by directly predicting bounding boxes and class scores from multi-scale feature maps, optimizing the trade-off between speed and precision. The YOLO series [10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20] has been at the forefront of one-stage detectors: YOLOv3 introduced multi-scale predictions with Darknet-53, while YOLOv7 enhanced feature extraction through E-ELAN networks and dynamic re-parameterized convolutions. YOLOv8 further advanced the field by adopting an anchor-free detection head and the C2f module, enabling multi-task integration with state-of-the-art performance in detection, segmentation, and classification. These innovations highlight the advantages of single-stage methods, including reduced computational complexity, faster inference speeds, and easier deployment on edge devices. Recent advancements have seen the development of numerous novel algorithms aimed at tackling the complexities of underwater object detection, including image blur, color distortion, dense small objects, and dynamic environmental interference. [PERSON] et al. [21] developed the SWIPENet+IMA framework, which enhances small object detection through high-resolution super-feature maps and reduces the impact of heterogeneous noise in underwater images using the Inverse Multi-class Adaboost algorithm. [PERSON] et al. [22] designed the SA-FPN, which uses a special backbone subnetwork to extract fine-grained features and incorporates Soft Non-Maximum Suppression (Soft-NMS) to optimize dense bounding box filtering. [PERSON] et al. [23] proposed a multi-scale feature pyramid and context enhancement mechanism, which fuses semantic information through a top-down upsampling pathway, significantly improving the robustness of multi-scale underwater object detection. [PERSON] et al.