# Developing a Dead Fish Recognition Model Based on an Improved YOLOv5s Model
## 6 Conclusions  
In this paper, a dead fish recognition model based on multi-scale feature fusion is proposed. The model uses the DWMConv module to replace the original Conv module, the C3-EMA module is proposed by embedding the EMA mechanism in C3, and the C3-Light module is proposed by using the DWConv convolution to replace the standard convolutional Conv in the C3 module. On the homemade dataset, the mAP\({}_{0.5}\), precision, recall, and F1-score of YOLO-DWM are 87.5%, 93.6%, 77.5%, and 84.8%, respectively. Params and FLOPs are 6.34 M, and 15.7 G, respectively. The following conclusions are obtained from the experiments described in this paper:  
(1) The model performance is improved by introducing DWMConv, a multi-scale feature fusion module based on depth-separable convolution, and C3-EMA, an efficient multi-scale attention mechanism. As can be seen from Table 1, all the indicators of the model are well improved. That is to say, the research from the aspect of multi-scale feature fusion can effectively improve the detection rate of dead fish on the water surface.  
(2) The C3-Light module effectively reduces the model parameters and computation under the condition that the accuracy is basically unchanged, and the Params and FLOPs are reduced by 0.53M and 0.5G after adding the YOLOv5s+DWMConv+C3-EMA model.  
(3) From Table 2 of the comparative experimental results, compared with Faster R-CNN, SSD, YOLOv3-tiny, YOLOv5s, YOLOv6n, and YOLOv8n, the mAP\({}_{0.5}\) improves by 8.13%, 12.3%, 5.9%, 4.5%, 4.5%, and 2.8%, respectively, while the F1 scores increased by 17.5%, 7.5%, 2.7%, 2.6%, 3.0%, and 0.4%. The improved algorithm outperformed these comparative algorithms.  
From the current study, it is shown that this improved method is feasible for enhancing the accuracy of dead fish detection at the surface. In future research, while improving the detection accuracy, the focus will be on model lightweight and nighttime dead fish identification for application in the real-time detection of dead fish in outdoor aquaculture farms.  
Conceptualization, B.L.; methodology, C.T. and X.X.; software, B.L. and J.W.; validation, B.L.; formal analysis, B.L.; investigation, B.L., X.X. and J.W.; resources, C.T.; data curation, B.L.; writing--original draft preparation, B.L.; writing--review and editing, B.L. and C.T.; visualization, B.L.; supervision, C.T.; project administration, C.T.; funding acquisition, C.T. and X.X. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Hunan Provincial Key R&D Program, Study on technology and equipment of engineering circulating water culture in self-floating assembled pond, grant number 2022 NK2028; Hunan Provincial Natural Science Foundation Upper-level Programs, grant number 2024 JJ5209; and the Scientific Research Project of the Hunan Provincial Education Department, grant number 22B0186. This work was supported by the China Scholarship Council (CSC) under grant no. 202308430207.  
Not applicable.  
Data Availability Statement: For data supporting the results of this study, please contact the author for communication. Due to privacy, these data have not been made public.  
Expressions of gratitude are extended to Kaitian New Agricultural Science and Technology Co. in Hunan Province, China, for their provision of the designated test site, and to [PERSON] and [PERSON] for their invaluable contributions to the present paper.  
The authors declare no conflicts of interest.