Imaging Sonar Detection and Occlusion-Corrected Abundance Estimation in High-Density Aquaculture

Real-time numerical abundance monitoring in high-density recirculating aquaculture systems (RASs) faces severe bottlenecks due to target occlusion saturation and spatial sampling mismatch. This study establishes an acoustic-statistical framework coupling deep learning detection with spatial statistical theory across sequential detection, correction, and scale expansion stages. To resolve systematic counting underestimation caused by target occlusion and acoustic shadowing, a Poisson coverage model incorporating morphological eccentricity (e) is formulated to statistically correct visual detection biases. To extrapolate localized acoustic observations to whole-pond fish abundance, a spatial-scale expansion model is developed based on localized roaming behavior and spatial stratification, parameterizing horizontal (α) and vertical (ν) distribution coefficients. Under controlled density gradients (20-100 individuals), the framework achieves a minimum relative estimation error of 9.58%. In commercial pond validation trials (6000-6500 individuals), post-correction global relative errors drop to 1.38-16.43%, substantially improving upon uncorrected visual detections (38.77-53.12%). This framework offers an automated, frame-level acoustic sensing workflow for monitoring fish abundance, providing foundational numerical data for feeding optimization and stock tracking in intensive aquaculture facilities.