This study proposes an Artificial Intelligence-based Real-time Catch Analysis System (AI-RCAS) for sustainable fisheries management. The AI-RCAS, implemented on a Jetson board, consists of fish recognition using YOLOv10, tracking with a ByteTrack algorithm optimized for marine environments, and a counting module. Experiments in actual fishing environments showed significant improvements, with species recognition rates of 74–81%. The system supports the efficient operation of the total allowable catch (TAC) system through real-time analysis, addressing the limitations of the existing Electronic Monitoring (EM) systems. However, challenges remain, including object-tracking difficulties and performance issues in unstable marine environments. Future research should focus on optimizing the fishing process, improving video processing, and expanding the dataset for better generalization.

The rapid depletion of fishery resources has led to the global implementation of Total Allowable Catch (TAC) systems. However, the current manual survey methods employed by land-based inspectors show limitations in accuracy and efficiency. This study proposes an automated system for fish species recognition and body length measurement, utilizing the RT-DETR (Real-Time Detection Transformer) model and ARCore technology to address these issues. The proposed system employs smartphone Time of Flight (ToF) functionality to measure object distance and automatically calculates the weight of 11 TAC-managed fish species by measuring their body length and height. Experimental results reveal that the RT-DETR-x model outperformed the YOLOv8x model by achieving an average mAP50 value 2.3% higher, with a mean recognition accuracy of 96.5% across the 11 species. Furthermore, the ARCore-based length measurement technique exhibited over 95% accuracy for all species. This system is expected to minimize data omissions and streamline labor-intensive processes, thereby contributing to the efficient operation of the TAC system and sustainable management of fishery resources. The study presents an innovative approach that significantly enhances the accuracy and efficiency of fishery resource management, providing a crucial technological foundation for the advancement of future fisheries management policies.

We propose a three-functional-layer decomposition framework for underwater acoustic (UWA) integrated sensing and communication (ISAC) preambles, instantiated as P5. Two spectrally separated chirp layers—hyperbolic frequency modulation (HFM) for wideband Doppler invariance and linear frequency modulation (LFM) for sub-meter ranging—are carried under a common constant-amplitude zero-autocorrelation (CAZAC) envelope that supplies cell identification and despreading against a root-blind attacker. Closed-form screening conditions constrain the layers to a near-orthogonal subspace, and direct cross-ambiguity measurement confirms the realized separation. In matched-filter Monte Carlo simulation, P5 meets the continuous-sensing target (range root mean square error ${\sigma }_R\le 1$ m at 10 dB signal-to-noise ratio) and has the smallest normalized matched-filter peak loss across twelve modeled UWA environments among four tested waveforms. Against four classical structure-aware attackers it stays below the strict $P_d\le 0.1$ low-probability-of-intercept target at 0 dB attacker-input SNR. A 10-seed, 11.2-million-parameter spectrogram ResNet-18 reaches $P_d=0.5$ against P5 at mean $+21.24$ dB total-energy SNR (95% CI $[+21.06,+21.42]$ dB) and $P_d=0.1$ at $+18.72$ dB ($[+18.45,+19.00]$ dB); these crossings are lower bounds on adversary capability, not a security guarantee. The integration also has explicit costs: composite peak-sidelobe level ($-7.60$ dB default, $-11.29$ dB optimized) remains inferior to equal-aperture single-waveform baselines, and sixteen-cell identification falls to ≤$0.07$ under a $+2$ dB near–far interferer. All-60-sounding WATERMARK replay further gives adverse P5^def–B5 losses of $-0.816$ dB on NOF1 (sounding-cluster 95% CI $[-1.021,-0.621]$ dB) and $-0.950$ dB on NCS1 ($[-0.977,-0.923]$ dB) after all waveforms are scaled into the same measured 8-kHz band. The evidence is therefore simulation dominant and supplemented by measured-channel replay of band-scaled variants; native-band transducer, pool, and sea-trial validation remain future work.