Research on Small Target Detection Method for Poppy Plants in UAV Aerial Photography Based on Improved YOLOv8

In response to the challenges in unmanned aerial vehicle (UAV)-based poppy plant detection, such as dense small targets, occlusions, and complex backgrounds, an improved YOLOv8-based detection algorithm with multi-module collaborative optimization is proposed. First, the lightweight Efficient Channel Attention (ECA) mechanism was integrated into the YOLOv8 backbone network to construct a composite feature extraction module with enhanced representational capacity. Subsequently, a Bidirectional Feature Pyramid Network (BiFPN) was introduced into the neck network to establish adaptive cross-scale feature fusion through learnable weighting parameters. Furthermore, the Wise Intersection over Union (WIoU) loss function was adopted to enhance the accuracy of bounding box regression. Finally, a dedicated 160 × 160 pixels detection head was added to leverage the high-resolution features from shallow layers, thereby enhancing the detection capability for small targets. Under five-fold cross-validation, the proposed model achieved [email protected] and [email protected]:0.95 of 0.989 ± 0.003 and 0.850 ± 0.013, respectively, with average increases of 1.3 and 3.2 percentage points over YOLOv8. Statistical analysis confirmed that these performance gains were significant, demonstrating the effectiveness of the proposed method as a reliable solution for poppy plant detection.