Sex identification in Procambarus clarkii using multi-dimensional feature fusion and enhancement

Accurate sex identification in Procambarus clarkii is essential for genetic breeding and aquaculture management, as it helps optimize population structure, improve reproductive efficiency, and support sustainable aquaculture development. However, manual identification is time-consuming, labor-intensive, and prone to errors, especially when subtle visual differences need to be distinguished. To address this problem, this study proposed SCM-DETR, a sex identification method for Procambarus clarkii based on multi-dimensional feature fusion and enhancement. A high-resolution imaging system was used to acquire two-dimensional images of Procambarus clarkii, and a labeled dataset, the Procambarus clarkii gonad dataset (PGD), was constructed. To improve identification performance, a multi-dimensional semantics and details fusion method (MSDM) was designed to integrate high-level semantic information with fine-grained detail features, thereby enhancing feature representation and localization accuracy. In addition, a channel-spatial focus network (CSFN) was introduced to capture discriminative multidimensional features, including texture and color, for more accurate identification of subtle sex-related differences. Experimental results showed that SCM-DETR-R18 achieved 95.8% mAP@0.50 and 64.6% mAP@0.50-0.95 on the PGD, improving by 1.9 and 1.1 percentage points over the baseline model, respectively. The AP values of female and male gonads reached 93.3% and 96.5%, with gains of 3.3 and 1.9 percentage points, respectively. Moreover, the proposed model had the lowest parameter count (21.11 M) among all compared methods. The results of this study demonstrate that SCM-DETR can effectively improve automated sex identification in Procambarus clarkii and has good potential for intelligent aquaculture applications.