Unmanned aerial vehicles (UAVs) for environmental monitoring typically rely on embedded platforms with limited computational capacity, which constrains the deployment of highly accurate yet computationally demanding object-detection models. To address this challenge and enable real-time image recognition under resource limitations, this study develops three lightweight neural network architectures based on the YOLOv11 framework. The proposed designs aim to significantly reduce computational complexity and parameter count while maintaining stable and reliable detection performance, thereby improving inference efficiency and deployment flexibility on UAV platforms. YOLOv11-M is selected as the baseline model due to its favorable trade-off between detection accuracy and inference speed. Three lightweight strategies are then proposed and evaluated. First, a Ghost Convolution approach replaces portions of standard convolution with low-cost linear operations, effectively reducing both parameter size and computational overhead during feature extraction. Second, MobileNetV4 is employed as the backbone network; its optimized bottleneck structures and attention mechanisms enable substantial model compression without compromising recognition performance. Third, a MobileOne architecture with reparameterization is introduced, in which multi-branch structures enhance feature learning during training and are subsequently merged into a single-path network for inference, thereby significantly reducing computational cost and improving practical deployability.
Zhang et al. (Thu,) studied this question.