Abstract: Population census of sea cucumbers can serve as the fundamental and operational metric in contemporary aquaculture. The essential data can also be used for production management, feed administration, and commercial transactions. However, manual sampling and counting cannot fully meet the large-scale production in recent years. Multiple operational challenges still remain in the industry. These conventional approaches can also suffer from three principal limitations: 1) Exorbitant labor requirements can lead to unsustainable operational costs; 2) Suboptimal counting speeds can result in processing bottlenecks during high-volume operations; 3) Inherent human error can cause measurement inaccuracies. The continuous expansion of sea cucumber farming has been exacerbated by the current production scales in the economic and logistical practice. It is the urgent industry demand for automated, intelligent counting for delivering accurate, real-time population data. In this study, an intelligent counting framework was presented using advanced machine vision on unmanned surface vehicles (USVs). The solution framework was integrated into three synergistic technical modules, including object detection, target tracking, and automated counting. Among them, the YOLOv8s-BB model was developed for optimal detection. The underwater object recognition was significantly improved after a key architectural adjustment. Firstly, the BiFormer convolutional attention module (BCAM) was designed to strategically combine a Bi-level routing with the spatial attention mechanism. This hybrid attention module was then integrated at the terminal layer of the YOLOv8s Backbone network. Secondly, the dedicated BiFormer modules were inserted after each C2f block in the Neck architecture. The feature extraction and fusion were enhanced for the underwater environments with variable lighting conditions and turbidity. In the tracking module, an advanced tracking algorithm, TriSORT, was featured by the triple-matching, in order to effectively solve the target limitations of the conventional ByteTrack in aquatic settings. Specifically, the TriSORT algorithm was implemented on a three-stage cascade matching between detection and predicted boxes. The high-confidence detection was introduced to enhance the tracking accuracy and robustness. The counting module was implemented to compare the traditional line-crossing and the inactive trajectory removal. The superior performance was achieved by dynamically filtering the transient detections. Only validated trajectories were maintained to eliminate the common counting artifacts prevalent in aquatic environments. Comprehensive experimental evaluations demonstrated that the superiority of the framework: 1) The YOLOv8s-BB detector was achieved in the state-of-the-art performance with 88.9% mean average precision (mAP), 77.8% recall, and 84.2% F1 score, thus outperforming existing YOLO model variants: YOLOv8s, YOLOv7-tiny, YOLOv9s, and YOLOv11s models; 2) The TriSORT tracking algorithm was enhanced the tracking robustness, with the 74.00% multiple object tracking accuracy (MOTA) and 85.03% IDF1 score, indicating the improvements of 6.55 and 5.54 percentage points, respectively, over ByteTrack; 3) The removal counting of the inactive trajectory was achieved in the superior performance with an average counting accuracy of 95.46% and a mean absolute error of 1.90. The superior performance was achieved in all evaluated metrics over conventional line-crossing counting. The great contribution was also gained in the aquaculture technology. A fully optimal detection-tracking-counting pipeline was established for the precise, efficient, and automated assessment of the sea cucumber population. The finding can provide reliable, data-driven support to the critical aquaculture operations, including biomass estimation, feeding optimization, and harvest planning. The potential applications can also be extended to marine species.