Design And Implementation Of Monitoring System For Aquaculture Fish And Crab Based On YOLOv5

Our country has been a big country of aquaculture,aquaculture industry occupies an important position in national economy.However,at present,most fishermen still use traditional methods to monitor or feed aquatic products,such as artificial visual feeding,which will not only consume a lot of manpower and material resources,but also may cause problems such as water eutrophication or slow growth of aquatic products due to inaccurate control of the feeding amount by aquaculture personnel.With the development of science and technology,the research on deep learning theory goes deeper and deeper,and the object detection method based on deep learning is gradually widely applied in all walks of life.However,intelligent equipment is rarely used in the field of aquaculture.In order to monitor aquaculture areas more effectively and improve the efficiency of large and layered aquaculture ponds,the thesis proposes to use the improved YOLOv5 network to detect and identify fish and crabs in aquaculture,and design and implement a monitoring system for aquaculture fish and crabs.The main work of this thesis is as follows:1.An improved multi-scale feature fusion network CRS-YOLOv5 is proposed.First,the network enhances its ability to extract image features by introducing a Ghost Bottleneck structure in the C3 module of YOLOv5 network,adding a CA attention mechanism,and using the Add operation to reduce the number of network parameters.Secondly,Rep Block structure is used in neck network to enhance network feature fusion.Finally,Focus structure is removed and Stem structure is used to reduce the computational complexity of the network.The experimental results show that compared with the YOLOv5 network,the m AP value of CRSYOLOv5 network increases from 89.8% to 93.8%,and the number of parameters and calculation amount decrease by 24.4% and 72.0%,respectively.2.The CRS-WSC-YOLOv5 fish and crab detection network was designed,and the weighted fusion algorithm based on WBF and SIo U loss function were used to screen the regression box and reduce the occurrence of multiple detection in the detection of fish and crab.The Cut Mix data enhancement method was used to expand the data set and improve the antiinterference capability of the network.The experimental results show that the CRS-WSCYOLOv5 fish and crab detection network has a good detection effect in the actual underwater environment,and the detection block location is accurate without redundancy,and the m AP value reaches 94.3%.3.Design and implement monitoring system for aquaculture fish and crab.The system includes two parts: hardware and software.The hardware system includes camera,GPS module,communication module,sensor,server,etc.The software system is mainly a cloud platform software system,through which the number and location of fish and crabs can be detected,aquaculture environment monitoring and other functions.The system is deployed on the cloud server for testing,and the test results show that the system has accuracy and real-time detection of targets,and can effectively meet the needs of actual breeding.