Abstract: In the integrated aquaculture system, a multi-species polyculture mode is commonly adopted, where freshwater economic species with ecological complementarity such as fish, crustaceans, and shellfish are co-cultivated in the same water body. To meet the differentiated demands for product specifications in the market, accurate sorting and processing according to biological species are required during the harvesting operation stage. This approach not only ensures the commercial value of various aquatic products and improves the efficiency at the sales end, but also optimize the management efficiency of the overall production and processing chain. In the classification and processing scenario of freshwater snail products, various snail species usually need to be accurately classified and graded for processing after fishing operations. The classification and detection of freshwater snail species are the basis for the automated processing of snail products, and it is of great significance in the industrialized cultivation, fishing, product processing, and classified sales of freshwater snails. Currently, machine vision technology based on deep learning is commonly applied to the classification and grading of agricultural products. However, in the classification operation link, the number of freshwater snails is usually huge, and as dense small targets, they are difficult to detect. Existing target detection algorithms still have deficiencies in perceiving dense small targets. Therefore, in response to the modernization needs of China's fishery industry, researching accurate and efficient detection methods for dense small target like freshwater snails is essential to promote automation in snail classification and processing. The development of automated aquaculture for freshwater snails is later than that of other aquatic organisms, with relatively few targeted automation and intelligence studies. Moreover, the algorithms described in relevant literature still have insufficient recognition effects for dense small targets of freshwater snails. In addition, different types of freshwater snails exhibit various shapes. When horizontal detection frames are used, a large amount of redundant information is included, leading to overlap significantly between frames. The use of Non-Maximum Suppression (NMS) may result in missed detections, which significantly impacts the model performance. This problem is particularly pronounced when freshwater snails are densely and overlappingly distributed, with subtle inter-class feature differences and complex backgrounds, their recognition performance is obviously insufficient. To effectively solve these problems, this paper innovatively proposes a dense small target detection algorithm for freshwater snails based on the improved YOLOv8-OBB algorithm. This algorithm processes the P2 feature layer through the introduction of SPDConv to obtain features rich in small target information, and fuses these features with the P3 layer. On this basis, the CSP and Omni-Kernel modules are combined for improved integration to obtain a new small target feature integration structure of COK, enhancing the network's perception ability for dense targets. The improved structure has increased the mAP0.5 index by 3.9%. Additionally,, an improved C2f-SREM module is proposed, incorporating parallel branches of SobelConv and additional convolution with a four-layer convolutional neural network and a triple residual connection architecture. This design greatly expands the global receptive field of the model and significantly enhances the context modeling ability, making the improved model more accurate in small target recognition. Compared with the original structure C2f, the improved module has increased the mAP0.5 index by 1.2%. From the perspective of the overall improved network model, the mAP0.5has increased by 11.6% compared to the original network, demonstrating obvious performance advantages. This research is of great significance for the development of the freshwater snail industry. In the industrialized cultivation of freshwater snails and the classification and grading processing of snail products after harvesting operations, the research results can provide reliable theoretical support, helping to promote the transformation and upgrading of the aquatic product processing industry such as freshwater snail classification and grading towards automation and intelligence, effectively improving industrial efficiency and increasing economic benefits.