 This paper proposes an improved algorithm for small target detection called YLOV5-SPP, which uses a CA attention module to better focus on task-specific important channels while weakening the influence of irrelevant channels. It also uses a metasealin activation function to adaptively learn to adjust the degree of linearity or non-linearity of the activation function based on the input data. Additionally, the SPD-CONV module is incorporated into the network model to address the problems of reduced learning efficiency and loss of fine-grained information due to cross-layer convolution in the model. The detection head is improved by using smaller, smaller target detection heads to reduce missed detections. This algorithm was tested on two datasets and compared against other state-of-the-art algorithms. Results showed that the proposed algorithm outperformed the baseline algorithms in terms of accuracy, demonstrating its effectiveness in small target detection. This article was authored by Hisu, Wenlongjiang, Fengxianlu, and others. We are article.tv. Links in the description below.