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.