[Objective] With the accelerated development of large-scale and intelligent aquaculture, accurate estimation of the body weight of individual Chinese mitten crabs is critical for tasks such as precise feeding, disease prevention, and optimization of harvest decisions. Traditional methods of manually catching and weighing crabs are time-consuming, labor-intensive, and can cause stress or injury to the crabs, while also failing to provide real-time monitoring. To address the challenges posed by turbid water conditions in aquaculture, which lead to poor image quality and difficulty in feature extraction, a method is proposed for estimating Chinese mitten crab weight that combines binocular vision with deep learning–based keypoint detection. This approach achieves high-precision detection of anatomical keypoints on the crab, providing new technical support for precision aquaculture and intelligent management. [Methods] Based on a lightweight YOLOv11 framework, in its C3K2 module, MBConv depthwise-separable convolutions were incorporated to significantly reduce computational complexity and improve feature extraction efficiency. An EffectiveSE channel attention mechanism was introduced to adaptively emphasize important channel-wise features. To further enhance cross-scale information fusion, a spatial dynamic feature fusion module (SDFM) was added. The SDFM adaptively and weightedly fused local spatial attention with global channel attention, enabling detailed extraction of crab shell edges and anatomical keypoints. The improved YOLOv11-ES model could simultaneously output the crab's bounding box, the positions of four anatomical keypoints, and the crab's sex classification in a single forward pass. In the 3D reconstruction stage, calibrated stereo camera parameters were used, and a sparse keypoint matching strategy guided by the crab's sex and spatial geometric constraints was employed. High-confidence keypoint pairs were selected from the left and right views, and the true 3D coordinates of the crab's carapace length and width were computed by triangulation. Finally, the obtained carapace length, width, and sex label data were fed into a two-layer back-propagation (BP) neural network to perform a regression prediction of the individual crab's weight. [Results and Discussion] To validate the effectiveness and robustness of the proposed method, a dataset of Chinese mitten crab images with annotated keypoints was constructed under varying water turbidity and lighting conditions, and both ablation and comparative experiments were conducted. The YOLOv11-ES achieved a mean average precision at intersection over union (IOU) threshold of 0.5 (mAP@50) of 97.2% on the test set, which was 4.4 percentage points higher than the original YOLOv11 model. The keypoint detection component reached an mAP@50 of 96.7%, which was 3.6 percentage points higher than that of the original YOLOv11 model. In comparative experiments, YOLOv11-ES also demonstrated significant advantages over other models in the same series. Moreover, in a full-system evaluation using images of 30 individual crabs, the mean absolute percentage error (MAPE) for carapace width measurements was only 2.68%, and for carapace length it was 1.48%. The Pearson correlation coefficients between the measured and manually obtained true values for both carapace length and width exceeded 0.977, indicating high accuracy in the 3D reconstruction and minimal measurement error. Experiments analyzing the influence of image quality on measurement accuracy showed that when the underwater image quality measure (UIQM) reached at least 1.5, the combined MAPE of carapace length and width errors could be kept below 5%. When UIQM reached at least 2.2, the MAPE dropped to about 1.9%. These results confirmed the robustness of the method against variations in water turbidity and lighting conditions. For weight regression prediction, the BP network trained on carapace length, width, and sex features achieved a mean absolute error (MAE) of 2.39 g and a MAPE of 7.1% on an independent test set, demonstrating high-precision estimation of individual crab weight. [Conclusions] The proposed method, which combines an improved YOLOv11 object detection network, binocular sparse keypoint matching, and a two-layer BP regression network, enabled high-precision, low-error, real-time, non-contact estimation of Chinese mitten crab weight in complex turbid aquatic environments. This approach featured a lightweight model, high computational efficiency, excellent measurement accuracy, and strong adaptability to varying environmental conditions. It provided key technical parameters for intelligent Chinese mitten crab farming. In the future, this approach could be extended to other aquaculture species and complex farming scenarios. Combined with transfer learning and online adaptive calibration techniques, its generalization capability could be further improved and integrated with intelligent monitoring platforms to achieve large-scale, all-weather underwater crab weight estimation, contributing to the sustainable development of smart aquaculture.