Exploring the Role of Activation Functions in YOLOv8 for Enhanced Performance

Exploring different activation functions can help in understanding their impact on the model's performance, stability, and convergence speed. It can also lead to discovering the most suitable activation function for specific tasks or datasets. This study investigated various activation functions for YOLOv8, specifically ReLU, Mish, Hardswish, PReLU, and FReLU, in comparison to the default activation function, SiLU. The CRDDC2022 dataset was employed to evaluate and compare the performance metrics of each activation function. The findings revealed that Mish and Hardswish performed similarly, achieving mean average precision scores of 0.449 and 0.439, respectively, indicating their potential as promising alternatives to SiLU. Conversely, PReLU and FReLU did not yield satisfactory results, highlighting challenges that warrant further research in this area.