  Hermit crabs play a crucial role in coastal ecosystems by dispersing seeds,
cleaning up debris, and disturbing soil. They serve as vital indicators of
marine environmental health, responding to climate change and pollution.
Traditional survey methods, like quadrat sampling, are labor-intensive,
time-consuming, and environmentally dependent. This study presents an
innovative approach combining UAV-based remote sensing with Super-Resolution
Reconstruction (SRR) and the CRAB-YOLO detection network, a modification of
YOLOv8s, to monitor hermit crabs. SRR enhances image quality by addressing
issues such as motion blur and insufficient resolution, significantly improving
detection accuracy over conventional low-resolution fuzzy images. The CRAB-YOLO
network integrates three improvements for detection accuracy, hermit crab
characteristics, and computational efficiency, achieving state-of-the-art
(SOTA) performance compared to other mainstream detection models. The RDN
networks demonstrated the best image reconstruction performance, and CRAB-YOLO
achieved a mean average precision (mAP) of 69.5% on the SRR test set, a 40%
improvement over the conventional Bicubic method with a magnification factor of
4. These results indicate that the proposed method is effective in detecting
hermit crabs, offering a cost-effective and automated solution for extensive
hermit crab monitoring, thereby aiding coastal benthos conservation.
