Abstract: The widespread mortality has often occurred in aquaculture systems in recent years, due to the high stocking densities. The presence of a few dead fish in tanks can also lead to the mass death of the overall population. In this study, the accurate and rapid detection of the dead fish was proposed to combine with the key point correction, according to their underwater posture. A series of experiments was conducted on the largemouth bass (Micropterus salmonids) under the culture mode of the round culture barrels. A dataset was then captured to detect the underwater dead fish. The MLP-Block (Multi-Layer Perceptron) was combined with the MSPCA (Multi-Path Coordinate Attention) mechanism to enhance the dynamic convolution. The MLPNet-Pose algorithm was used to group the decoupling head for the fused features from the path aggregation network. Both target detection and key point detection outputs were realized after feature fusion. Furthermore, a key point correction was applied to classify the posture features of the underwater fish. Thereby, the accurate identification of dead fish was achieved after correction. The posture behavior of the fish after death was obtained as the key indicators for detection, such as abnormal floating or reduced movement. A dataset was specifically constructed to detect the dead fish. The target detection was also integrated with the key point detection. The high precision was obtained to differentiate between live and dead fish. The MLPNet-Pose with the dynamic convolution was enhanced by a multi-path coordinate attention mechanism. Some subtle differences in posture between live and dead fish were effectively captured and then processed after optimization. Moreover, the decoupling head improved the efficient fusion of features and the accuracy of the detection. The key point correction was used to refine the classification of the fish postures. There were some variations in the fish's orientation and movement. The reliable identification of the dead fish was realized under complex and dynamic underwater environments. Experimental results show that the improved algorithm was achieved in the detection accuracies of 99.1% for the live fish and 96% for the dead fish on the test dataset. The key point detection demonstrated high precision, high speed, and lower parameter count, particularly suitable for real-time applications. A solid theoretical and technical solution was offered to identify the key point and underwater dead fish for the optimal feeding strategy. In summary, the improved detection of the key point can offer a highly accurate and efficient solution to identify the underwater dead fish in aquaculture systems. The target detection and key point correction were combined to operate effectively in real time, particularly for the robust and scalable approach in modern aquaculture. The findings can also provide valuable technical insights for the detection of the dead fish, feeding strategy, and underwater fish monitoring.