Cross-Scale Feature Enhancement for Cotton Seedling Detection in UAV Images

Deep-learning-based object detection methods have achieved significant results in unmanned aerial vehicle (UAV) crop seedling image detection. However, when there are differences in the shape characteristics and sizes of seedlings within datasets, the performance of the detector tends to decrease. Existing methods typically rely on specific datasets, ignoring the problem of feature disparities caused by complex and variable field environments. In this letter, a cotton seedling detection framework based on cross-scale feature enhancement (CFE) is presented. CFE reconstructs features through multilevel feature aggregation (MFA) and enhances the reconstructed feature layers using global contextual dependencies extracted by transformer encoder, enabling the sharing of long-range dependency information across different feature spaces. Furthermore, a fuzzy dynamic weighted loss (FDWLoss) strategy is proposed to balance the targets for difficult-to-identify in the training process. Experimental results demonstrate a significant improvement in detection performance and generalization ability on six datasets of the proposed model, which is particularly suitable for cotton seedling detection in various field environments.