AMS-YOLO: Asymmetric Multi-Scale Fusion Network for Cannabis Detection in UAV Imagery

Cannabis is a strictly regulated plant in China, and its illegal cultivation presents significant challenges for social governance. Traditional manual patrol methods suffer from low coverage efficiency, while satellite imagery struggles to identify illicit plantations due to its limited spatial resolution, particularly for sparsely distributed and concealed cultivation. UAV remote sensing technology, with its high resolution and mobility, provides a promising solution for cannabis monitoring. However, existing detection methods still face challenges in terms of accuracy and robustness, particularly due to varying target scales, severe occlusion, and background interference. In this paper, we propose AMS-YOLO, a cannabis detection model tailored for UAV imagery. The model incorporates an asymmetric backbone network to improve texture perception by directing the model’s focus towards directional information. Additionally, it features a multi-scale fusion neck structure, incorporating partial convolution mechanisms to effectively improve cannabis detection in small target and complex background scenarios. To evaluate the model’s performance, we constructed a cannabis remote sensing dataset consisting of 1972 images. Experimental results show that AMS-YOLO achieves an mAP of 90.7% while maintaining efficient inference speed, outperforming existing state-of-the-art detection algorithms. This method demonstrates strong adaptability and practicality in complex environments, offering robust technical support for monitoring illegal cannabis cultivation.