## 5 Conclusions  
In this paper, we propose a new lightweight ear recognition method named YOLOv5s-MG, which uses the lightweight network MobileNetV3 and Ghostnet to lightweight YOLOv5s. Due to the improved YOLOv5s-MG method, the feature extraction of the backbone network of YOLOv5s and the feature fusion of the neck network of YOLOv5s are lightweight at the same time, which greatly reduces the number of parameters, calculations, and model size of the network, which can greatly improve the real-time performance of the method. The proposed YOLOv5s-MG method is tested on the testing set of three distinctive human ear datasets--CCU-DE, USTB, and EarVN1.0--and evaluated by four performance indicators: [EMAIL_ADDRESS], params, GFLOPS, and model size. Quantitative results show that the proposed method is superior to the other five methods in terms of real-time performance. Compared with YOLOv5s, the params, GFLOPS, and model size of this method on the CCU-DE dataset are increased by 69.63%, 77.44%, and 68.61%, respectively; the same three parameters on the USTB dataset are increased by 68.12%, 75.15%, and 67.31%, respectively; the same three parameters on EarVN1.0 dataset are increased by 69.53%, 77.44%, and 68.32%, respectively. Compared with the best results of the YOLOv5s-V3, YOLOv5s-V2, and YOLOv5s-G methods, the params, GFLOPS, and model size of this method on the CCU-DE dataset are increased by 35.94%, 41.27%, and 36.76%, respectively; the same three parameters on the USTB dataset are increased by 35.29%, 38.24%, and 35.57%, respectively; the same three parameters on EarVN1.0 dataset are increased by 35.63%, 42.19%, and 36.36%, respectively. For the recognition accuracy, that is, the [EMAIL_ADDRESS] value, there are different performances for different human ear datasets. In the case of complete convergence of the model, the recognition rate of the six methods is above 99% on the testing set of CCU-DE, but the [EMAIL_ADDRESS] value of the YOLOv5s-MG method is 0.2% lower than that of YOLOv5s; on the testing set of USTB, the recognition rate of the six methods except YOLOv7 is 100%; on the testing set of EarVN1.0, the ear recognition rate of the six methods is above 81% and the [EMAIL_ADDRESS] value of the YOLOv5s-MG method is 85.5%, which is 2.7% lower than that of YOLOv5s and 3% lower than that of the YOLOv5s-G method. For FPS, the proposed method YOLOv5s-MG in this paper is superior to the other five methods. The qualitative results demonstrate that YOLOv5s-MG has the same or slightly decreased ear recognition accuracy as YOLOv5s, YOLOv5s-V3, YOLOv5s-V2, YOLOv5s-G, and YOLOv7 methods, but the params, GFLOPS, and model size of this method are much smaller than those of other methods. The improved YOLOv5s-MG lightweight network proposed in this paper has good real-time performance and can meet the real-time requirements in the actual human ear recognition application system. However, for human ear datasets, such as EarVN1.0, when the real-time performance is improved, the human ear recognition rate may decrease. The next step will be to further study it to improve the human ear recognition rate.  
Conceptualization, Y.L. and D.P.; methodology, Y.L. and D.P.; software, D.P.; validation, J.Q. and Z.F.; investigation, Z.F.; data curation, D.P.; writing--original draft preparation, D.P.; writing--review and editing, Y.L.; project administration, Y.L.; funding acquisition, Y.L. All authors have read and agreed to the published version of the manuscript.  
This research was supported in part by the funds of the Science Technology Department of Jilin Province [NO: 2020 LY402L10] and in part by the funds of the Education Department of Jilin Province [NO: 2022 LY502L16].  
**Institutional Review Board Statement:** Not applicable.