基于变分推理半监督学习的玉米雄穗实例分割方法OA

Aiming to address the problems of time-consuming and labor-intensive annotation in corn tassel instance segmentation,susceptibility to subjective factors,and high prediction uncertainty caused by complex field scenes,an adaptive variational-inference Bayesian instance segmentation model(AVB-IS)was proposed.AVB-IS integrated Bayesian probabilistic analysis with deep learning,learned the feature distribution of corn tassels through variational inference,and optimized the latent space with adaptive priors,enabling the model to predict uncertainty in a single forward pass.AVB-IS leveraged uncertainty information to enhance feature representation,improving pseudo-label quality and model robustness.Experimental results demonstrated that AVB-IS achieved an effective balance between performance and efficiency across diverse backbone networks,attaining an average precision(AP)of 49.35%and delivering instance segmentation performance that surpassed mainstream models.Moreover,adjusting the KL divergence weight enabled faster and more stable convergence.Under the framework of semi-supervised learning with the AVB-IS model(AVB-IS-SSL),the model achieved performance comparable to full supervision using only 50%of the annotated data,substantially reducing dependence on manual labeling.The research result can offer a robust technical solution for intelligent maize tassel detection and hold significant value for advancing agricultural intelligence.