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Although object detection methods based on deep learning have achieved great success in recent years, they are not effective in small target detection. In order to solve the problem of low recognition rate caused by factors such as low resolution of UAV viewpoint images and little valid information, this paper proposes an improved algorithm based on the YOLOv5s model, called YOLOv5s-pp. First, to better suppress interference from complex backgrounds and negative samples in images, we add a CA attention module, which can better focus on task-specific important channels while weakening the influence of irrelevant channels. Secondly, we improve the forward propagation and generalisation of the network using the Meta-ACON activation function, which adaptively learns to adjust the degree of linearity or nonlinearity of the activation function based on the input data. Again, 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. Finally, the detection head is improved by using smaller, smaller-target detection heads to reduce missed detections. We evaluated the algorithm on the VisDrone2019-DET and UAVDT datasets and compared it with other state-of-the-art algorithms. Compared to YOLOv5s, mAP@.5 improved by 7.4% and 6.5% on the VisDrone2019-DET and UAVDT datasets, respectively, and compared to YOLOv8s, mAP@.5 improved by 0.8% and 2.1%, respectively. For improving the performance of the UAV-side small target detection algorithm, it will help to enhance the reliability and safety of UAVs in critical missions such as military reconnaissance, road patrol and security surveillance.<\/jats:p>","DOI":"10.3390\/rs15143583","type":"journal-article","created":{"date-parts":[[2023,7,18]],"date-time":"2023-07-18T01:35:16Z","timestamp":1689644116000},"page":"3583","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["Unmanned Aerial Vehicle Perspective Small Target Recognition Algorithm Based on Improved YOLOv5"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2809-2237","authenticated-orcid":false,"given":"He","family":"Xu","sequence":"first","affiliation":[{"name":"School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210023, China"},{"name":"Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks, Nanjing 210023, China"},{"name":"Jiangsu HPC and Intelligent Processing Engineer Research Center, Nanjing 210023, China"}]},{"given":"Wenlong","family":"Zheng","sequence":"additional","affiliation":[{"name":"School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210023, China"},{"name":"Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks, Nanjing 210023, China"}]},{"given":"Fengxuan","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210023, China"},{"name":"Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks, Nanjing 210023, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5026-5347","authenticated-orcid":false,"given":"Peng","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210023, China"},{"name":"Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks, Nanjing 210023, China"},{"name":"Jiangsu HPC and Intelligent Processing Engineer Research Center, Nanjing 210023, China"}]},{"given":"Ruchuan","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210023, China"},{"name":"Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks, Nanjing 210023, China"},{"name":"Jiangsu HPC and Intelligent Processing Engineer Research Center, Nanjing 210023, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. 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