Improved YOLOv5 Model Based on the Pyramid Structure of Dilated Convolutions
Jinghua Wu, Zijun Deng · 2023
With the rapid development of deep learning, there have been numerous object-detection algorithms based on deep learning. However, during the study of the YOLOv5 model, it was found that the SPP-based feature extraction structure may lose some details during the max-pooling process. Additionally, the LeakyReLU activation function lacks sufficient smoothness, and the multi-scale feature fusion using$\mathbf{FPN+PAN}$structure in its Neck part is not sufficient. To address these issues, this paper proposes an improved YOLOv5 model based on the pyramid structure of dilated convolutions, which introduces dilated convolutions with channel attention mechanism based on the SPP structure, and makes some adjustments in activation function and Neck part. Comparative experiments on the PASCAL VOC2007 and VOC2012 benchmark datasets show that the proposed model achieves higher detection accuracy.