Artificial intelligence (AI)–based coral monitoring can provide a transformative alternative to expert-dependent and labor-intensive surveys, holding significant ecological value for fragile coral ecosystems. However, coral detection algorithms remain constrained by limited fine-grained taxonomic datasets, edge-device capacity, and the complexity of coral texture feature extraction. To address these challenges, we propose CoralGrad-LiteNet (CG-LiteNet), a lightweight coral detection framework for efficient recognition. Key AI contributions: a Gradient-Aware Hierarchical Feature Fusion Module (GA-HFFM), which employs gradient convolution networks for multi-scale feature extraction, expanding receptive fields to 94.2% while preserving fine textures; Slim-Backbone and Neck reconstruction, coupled with the proposed Dynamically Anchored Distribution-Aware Head (DADH) and Loss function optimization; and Diffusion Model–based image generation modules that overcome the coral data barrier by constructing the Sanya-Coral dataset and expanding it into Sanya-Coral AI-Enhanced with an 82.5% scale-increase. Experiments demonstrate that CG-LiteNet surpasses state-of-the-art (SOTA) detectors in this domain. Diffusion-based augmentation yields an average +6.44% mean Average Precision across intersection over union thresholds from 0.50 to 0.95 (mAP50–95) across all coral species, with a peak +14% gain on Favites . CG-LiteNet contains only 2.1 million (M) parameters and 5.4 billion floating point operations per second (GFLOPs), reducing size and computation by 18.6% and 14% versus the baseline, while achieving +3.6% mAP50 and +2.8% mAP50–95 on Sanya-Coral, and 87.1% mAP50 on the AI-Enhanced dataset. Overall, CG-LiteNet provides an efficient and scalable solution for coral detection while pioneering a novel dataset enhancement paradigm to advance fine-grained coral recognition. Code and datasets are available at: https://github.com/yangchangen-s/CoralGrad-LiteNet . • Two controllable diffusion-based modules that synthesize photorealistic coral images. • Two high-quality coral datasets have been constructed to advance coral recognition. • A lightweight multi-scale framework tailored for intricate texture coral recognition. • A hybrid multi-scale detection head incorporates the coupled and decoupled paradigms. • Bounding box regression loss function balances target scales and accelerates training.
Yang et al. (Wed,) studied this question.