AAMS-YOLO: enhanced farmland parcel detection for high-resolution remote sensing images
Binyao Wang, Yanan Zhou, Weiwei Zhu, Li Feng, Jinke He, Tianjun Wu, Jiancheng Luo, Xin Zhang
Abstract
Detecting farmland parcels in high-resolution remote sensing images is challenging in smallholder farming systems in China, characterized by fragmented plots, irregular shapes, and varying scales. To improve detection accuracy in these contexts, this study proposes AAMS-YOLO, a YOLO-based farmland parcel detection model. In the feature extraction stage, the model incorporates an Adaptive Mix Attention (AMA) Block, balancing robust feature extraction with low computational overhead through spatial mixing and Efficient Multi-Scale Attention (EMA). During feature enhancement, to effectively detect targets of different scales, the Attentional Scale Sequence Fusion with P2 network (ASFP2Net) integrates the Triple Feature Encoder (TFE) module and Scale Sequence Feature Fusion (SSFF) module. In the prediction stage, a Multi-Scale Attention Head (MSAHead) enhances adaptability through multi-scale attention mechanisms. Extensive experiments on a custom-built dataset validate AAMS-YOLO's effectiveness, demonstrating notable enhancements over the baseline in mAP0.5 (2.6%) and mAP0.5:0.95 (2.2%) and surpassing other state-of-the-art algorithms. The proposed model excels in detecting small and densely overlapping objects through advanced feature fusion and multi-scale processing strategies.