Enhanced YOLOv10 Framework Featuring DPAM and DALSM for Real-Time Underwater Object Detection

Recently, spotting underwater objects has been increasingly difficult due to the complexities of marine environments and varied visibility conditions. YOLOv10 is notable for its effective, robust architecture, featuring significant components: advanced backbone networks and enhanced feature pyramid networks that also deliver anchor-free detection. In delivering YOLOv10, we enhance it with dual partial attention mechanism (DPAM) and dual adaptive label assignment with sun glint removal module (DALSM) along with marine fusion loss (MFL). With DPAM, the latest refinement processes for conservation focus on feature extraction to account for key highlights and in the scene, include temporal context, both critical for interpretation of the dynamic realm below the ocean. The prefix with DALSM involves adaptive dual label assignment and techniques for sun glint removal. The marine fusion loss (MFL) provides an object detection prediction that combines both binary cross-entropy loss and complete intersection over union (CIoU) loss to enhance bounding box localization while also including spatial context to incorporate important underwater features. With the experiments, we attenuate enhancements with device gradient clipping, model checkpointing, and advanced augmentation processes to gain 3.04% improvement in mean Average Precision (mAP). These enhancements mitigate perceived challenges of underwater detection while enhancing knowledge of understanding marine life.