MILLET EAR DETECTION METHOD IN UAV IMAGES BASED ON IMPROVED YOLOX

Rapid and accurate detection of millet ears is essential for yield estimation and phenotypic studies. However, traditional detection methods primarily rely on manual observation, which are both subjective and laborintensive. To address this issue, this study employed Unmanned Aerial Vehicle (UAV) for image data collection of millet ears and proposed the YOLOX-CBAM-EIoU model to facilitate real-time detection, focusing on challenges such as small millet ears size, dense distribution, and severe occlusion in the dataset. Firstly, the Mosaic data augmentation technique was employed to enhance the diversity of the dataset. Subsequently, the CBAM attention mechanism was incorporated between the Neck and Prediction layers of YOLOX, enabling the reallocation of channel weights to enhance the extraction of fine-grained features and deeper semantic information. Additionally, EIoU Loss was utilized as the loss function for bounding box regression to mitigate missed detections in dense scenes. The improved model achieved an average precision (AP) of 90.30%, a 6.44 percentage point increase over the original YOLOX model, significantly enhancing detection performance for densely distributed millet ears. The improved model also demonstrated a Precision of 91.01%, Recall of 89.45%, and F1-score of 90.22, highlighting strong robustness and generalization capabilities. These findings substantiate the efficacy of the YOLOX-CBAM-EIoU model in improving detection performance under dense distribution and occlusion conditions, providing valuable technical reference for further UAV-based analyses of millet ears phenotypes and yield predictions.