NSC-YOLOv8: A Small Target Detection Method for UAV-Acquired Images Based on Self-Adaptive Embedding

Existing drone image processing algorithms for small target detection in Unmanned Aerial Vehicle (UAV) aerial images struggle with challenges like missed detection of small objects, information loss from downsampling, loss of low-dimensional features, and information drop of contextual features. In order to alleviate the four problems just mentioned, we propose a self-adaptive small target detection method, NSC-YOLOv8, based on the YOLOv8 model. First, we introduce a small target detection head that enhances the model’s ability to fuse shallow and deep features, effectively handling low-pixel targets. Second, a Non-lossy Downsampling Block (NDB) is introduced into the backbone, which optimizes the detection accuracy of small targets in large scenes through dimensional transformation. In addition, we introduce a Self-Adaptive Embedding Block (SAEB) based on low-dimensional information, which enhances the comprehensive performance of the model by expanding the local sensing field to enhance the focus on important contextual information. Finally, we design a Content-Aware Resampling Block (CARB), which is able to enhance the model’s ability to recognize small targets by resampling low-dimensional features. Experiments on the VisDrone2019-DET dataset show that NSC-YOLOv8s improves target detection accuracy over YOLOv8s, with an 11.7% increase in [email protected]. Additionally, removing a large detection head and adjusting the bottom-up layers reduces NSC-YOLOv8s’ parameters by 1.2 M compared to YOLOv8s. Therefore, NSC-YOLOv8 shows better performance in small target detection for UAV imagery.