LMBC: Low‐Power Marine Benthos Counting Framework for Underwater Robotic Real‐Time Applications

ABSTRACT Accurate and efficient marine benthos counting is vital for aquaculture management and ecological monitoring, yet it remains highly challenging in underwater environments characterized by limited visibility, cluttered backgrounds, and the constraints of low‐power robotic platforms. This paper proposes a low‐power‐deployed marine benthos counting (LMBC) framework, specifically designed for real‐time, robust, and energy‐efficient enumeration on embedded underwater robots. The LMBC framework integrates three task‐oriented modules: (i) a lightweight prediction head optimized detector (LPHOD) that enhances tiny benthos detection while maintaining low computational complexity, (ii) a confidence ranking–cascade matching tracker (CRCMT) that improves identity preservation under occlusions and fluctuating detection confidence, and (iii) an accurate classification counting module that refines final counts by filtering spurious tracks and enforcing temporal consistency. Quantitative evaluations demonstrate that the proposed LPHOD achieves a detection accuracy of 86.2% [email protected] on the DUO data set, while the CRCMT attains a HOTA score of 57.5 with substantially reduced identity switches compared with representative trackers. End‐to‐end counting experiments further show that LMBC achieves a root mean square error of 13.8 and a mean absolute percentage error of 17.5%, outperforming baseline tracking‐based counting schemes. Implemented on an NVIDIA Jetson Xavier NX, the complete framework operates in real time at 13.3 FPS, validating its suitability for field‐deployed autonomous underwater robots in aquaculture and ecological monitoring scenarios.