Sinohyriopsis cumingii is one of the economically important freshwater mussels in the pearl aquaculture industry. Phenotypic traits of S. cumingii can be expected to evaluate the individual growth performance. Germplasm resources are identified to implement precise genetic breeding. However, conventional manual measurements cannot fully meet the scalability and applicability of the large-scale production in intelligent aquaculture, due to their labor-intensive, time-consuming, and highly susceptible to subjective errors. In this study, an improved measurement was proposed for the non-destructive, rapid, and accurate acquisition of phenotypic parameters using YOLOv8n, termed YOLOv8n-CBM. 1) An integrated phenotypic measurement for S. cumingii was constructed to combine the dynamic transmission, machine vision, and digital image processing. The system comprised a conveyor device, a high-precision industrial camera, and an image processing module. The mussel samples were automatically transported into the imaging area, thus enabling standardized image acquisition and high-throughput phenotypic measurement under continuous dynamic conditions. 2) Three targeted improvements were implemented in the original YOLOv8n network architecture, according to the characteristics of mussel images. In the backbone network, four convolutional block attention modules (CBAM) were embedded after each C2f block to enhance the extraction of contour edges and local features of mussel samples, while effectively suppressing irrelevant background interference. In the neck network, the bidirectional feature pyramid network (BiFPN) was introduced to strengthen bidirectional fusion of multi-scale features for the targets of different sizes and postures. Meanwhile, the original C2f module was replaced with a multi-scale dilated attention (MSDA) module to expand the network’s receptive field for the local fine-grained and global contextual information. Finally, the key phenotypic parameters were extracted, including shell length, full height, shell height, and radial rib length of the buttock angle, according to the geometric relationship between rotated bounding boxes and biological key points. A series of experiments was conducted on a dataset of 50 manually annotated S. cumingii samples with diverse sizes and postures. The results show that the mean average precision (mAP50-95) of the YOLOv8n-CBM model reached 98.2%, indicating the rotated object detection performance over the original YOLOv8n model. The average localization deviation of biological key points was less than 2.0 mm, indicating the high precision in feature detection. The mean absolute errors (MAE) of shell length, full height, shell height, and radial rib length of the buttock angle were 1.51, 1.08, 1.019, and 1.998 mm, respectively. In all groups stratified by different shell lengths and full heights, the measurement errors of YOLOv8n-CBM were consistently lower than those of the original YOLOv8n model, with the maximum absolute error within 2.879 mm. Measurement accuracy and robustness were effectively improved with diverse morphologies and postures. In conclusion, the reliable technical approach was used to realize the rapid, accurate, and non-destructive acquisition of phenotypic traits in S. cumingii. Shellfish growth evaluation and genetic breeding can be expected to support the transition of the pearl industry from empirical farming to data-driven and intelligent aquaculture. The findings can also offer a valuable reference for phenotypic measurement in the molluscan species.