UAVEL-YOLO: An Efficient and Lightweight Target Detection Method for Aerial Imagery Captured by UAVs

To address the challenge of target detection in UAV aerial images, which is exacerbated by large scale variations and complex backgrounds, this paper proposes a novel target detection model named UAVEL-YOLO. First, we design a lightweight Self-Adaptive Multi-Scale Contextual Perception Attention (SAMCPA) mechanism that enables the network to more effectively capture contextual information in the image and focus on more important regions, thereby enhancing the perceptual and interpretive abilities of the model. Second, we propose a Dual Branch Linear Separable Kernel (DBLSK) module, which not only suppresses the exponential growth of the parameter count but also provides richer gradient flow information. Moreover, to better detect small, dense, and variably sized objects, we incorporate a P2 detection head to enhance the model’s ability to perceive small targets. The proposed model is evaluated on the VisDrone2019 dataset. Compared with YOLO11s, our model improves [email protected], $\mathrm{mAP} \text{@} .5. .95$, Precision, and Recall by 6.5 %, 4.3 %, 3.9 %, and 5.5%, respectively, while reducing the model’s parameter count by 66.96%.