A UAV‐based deep learning pipeline for intertidal macrobenthos monitoring: Behavioral and age classification in Tachypleus tridentatus

Intertidal macrobenthos are vital bioindicators of coastal ecosystem health due to their ecological roles, limited mobility, and sensitivity to environmental disturbances. However, traditional field‐based monitoring methods are time‐consuming, spatially restricted, and unsuitable for large‐scale ecological surveillance. Integrating unmanned aerial vehicles (UAVs) with deep learning offers a promising alternative for high‐resolution, cost‐effective monitoring. Yet, species‐specific object detection frameworks for mobile macrobenthic fauna remain underdeveloped. Tachypleus tridentatus, an endangered “living fossil” with over 430 million years of evolutionary history, serves as a flagship species for intertidal conservation due to its ecological significance and biomedical value. This study develops a customized deep learning pipeline for monitoring T. tridentatus, combining UAV‐based image acquisition, automated detection, and ecological trait inference. We constructed the first UAV‐derived dataset of juvenile T. tridentatus (n = 761) and implemented a convolutional autoencoder for unsupervised behavioral classification, achieving 96% accuracy in distinguishing buried from exposed individuals. A YOLO‐based detection model was optimized using lightweight pruning and a high–low frequency fusion module (HLFM), improving detection accuracy (mAP@50 increased by 1.74%) and computational efficiency. Additionally, we established robust regression models linking crawling trace width to prosomal width (R2 = 0.99) and prosomal width to instar stage (R2 = 0.91). The inferred instar stages showed no significant deviation across datasets, validating their use as indicators of age structure. By bridging species‐level detection with population‐level ecological inference, this study provides a scalable, field‐deployable framework for monitoring T. tridentatus and other intertidal macrobenthic taxa. The approach supports data‐driven conservation strategies and enhances our capacity to assess the status of endangered coastal species in complex intertidal environments.