FSC-YOLOv5s Model Integrating FMix and Attention Stacking States

Abstract

In terms of target detection, the FSC-YOLOv5s algorithm is designed to address the weaknesses and limitations of the YOLOv5s algorithm in handling small data sets. When data acquisition is limited, the use of data augmentation can expand the number of feature points without increasing the original data volume, bringing a positive gain effect to the detection effect, which is the most optimal choice for lightweight networks. First, a mixed-sample data enhancement method FMix is introduced to enhance the data volume during data preprocessing, which can reduce the impact of small data sets on the detection accuracy. Second, the network structure of the YOLOv5s algorithm is improved by adding a SimAM parameter-free attention layer that can unify the weights when obtaining the network output content. The attention of the model to the key features of the data can be enhanced without adding an additional number of parameters. At the same time, the CBAM attention layer is added to the backbone network part to further enhance the in-depth learning of limited data features by automatically extracting important features and suppressing minor features through learning. Improving the screening ability of important features through the attention mechanism can effectively improve the integrity of detection targets. Then, it is combined with SGD, Adam, and Adamw optimization algorithms, respectively, to select optimizers that can improve the computational efficiency and adaptability of FSC-YOLOv5s selectively. Finally, the experiments showed that FSC-YOLOv5s improved the mAP50-95 by 30.3% and 5.1% on both data-sets, respectively, verifying the effectiveness of the FSC-YOLOv5s algorithm.