The juvenile fish cultivation stage is a very critical stage in the aquaculture process, and real-time monitoring and statistics of the number of juvenile fish is very important for the management of the aquaculture process. However, there are small targets, occlusion, and overlapping phenomena in cultivation stage, which seriously hinder the accurate detection and counting of juvenile fish. Conventional horizontal box detection has the phenomenon of feature reuse between targets in the face of severe occlusion or overlap. Therefore, this study tried to use the rotating box detection model to explore its ability to solve occlusion and overlap problems. First of all, this study constructed a data set of two kinds of juvenile fish in the incubation stage (including Brocarded Carp and Carp). Secondly, on the basis of Oriented RepPoints, the ECA attention mechanism is introduced to strengthen the feature map output at the end of the backbone feature extraction network, and an improved rotation box detection model EORNet is obtained. Finally, based on EORNet, the actual modeling effects (counting) of rotation box and horizontal box are compared to further explore the advantages of rotation box in mitigating occlusion and overlap problems. The results showed that the Recall and Precision of the rotation box detection model in the detection task reached 0.978 and 0.905 respectively, and the R2 of the counting model in the counting task reached 0.937. The rotating frame detection method has been proved to be superior to the horizontal frame detection method in mitigating occlusion and overlap problems, and the rotating box detection is more conducive to capturing the key feature information of the target object, and then achieving accurate prediction. In addition to counting, the rotating box detection method is also expected to be used to accurately estimate the length, width and weight of aquaculture objects in the process of aquaculture.