ALW-YOLOV8n:A Lightweight underwater detector Enhanced by Attention mechanism, ADown Block and Wise-WIoU on YOLOv8n

To date, general object detection methods have made significant progress in recognizing terrestrial scenes; however, identifying underwater organisms in underwater environments presents numerous challenges. The quality of underwater images is often affected by light attenuation and scattering, leading to blurring and loss of detail in the images. To address these challenges and further improve the accuracy of underwater object detection, this paper proposes an underwater object detection model based on an improved YOLOv8n, called ALW-YOLOv8n. Firstly, the ADown module is used to replace certain convolutional modules in the network, effectively reducing feature loss during the down-sampling process while also lowering computational costs. Secondly, in the backbone network, the LSKA module is integrated into the SPPF module, further enhancing the model's feature fusion capability. Finally, to address the limitations of the loss function, the CIoU loss function is replaced with the Wise-WIoU loss function to improve detection accuracy.The experimental results show that ALW-YOLOv8n performs exceptionally well on the URPC dataset, achieving an mAP@0.5 of 82.1%, which is 2.0% higher than the original YOLOv8n model, while reducing the number of parameters by 4.81%. Additionally, on the S-UODAC2020 dataset and the Aquarium dataset, ALW-YOLOv8n achieved 68.8% and 71.7% mAP@0.5, respectively. Finally, extensive experiments were conducted, and the results demonstrate that the model has broad applicability and generalization capabilities across different underwater datasets.