SemiXAI-Net: XAI-driven semi-supervised object detection via iterative pseudo-label refinement and spatiotemporal consistency

In recent years, object detection has become an indispensable component in various critical applications, ranging from autonomous driving to surveillance systems. However, achieving high detection accuracy alone is insufficient; models must also be transparent and interpretable to foster trust and ensure reliable decision-making, especially in safety-critical domains. Traditional supervised approaches require large amounts of labeled data, which is often expensive and time-consuming to obtain. Additionally, these models typically function as “black boxes,” providing limited insight into their decision processes. This article introduces SemiXAI-Net, a semi-supervised learning framework optimized for object detection tasks while incorporating explainable AI (XAI) techniques to enhance transparency. By utilizing a teacher-student architecture, the framework iteratively refines pseudo-labels, effectively improving both model performance and interpretability. The experimental evaluation was performed using the Common Objects in Context (COCO) dataset (a real-world benchmark) alongside synthetic datasets to simulate semi-supervised conditions. A labeled subset of COCO images was combined with unlabeled data (both from COCO’s unannotated images and synthetic sources) for training. The framework was implemented on NVIDIA Tesla V100 GPUs using PyTorch, with hyperparameters optimized for stability (batch size: 32, learning rate: 0.001) and efficiency. To enable real-time interpretability, the student model integrated Gradient-weighted Class Activation Mapping (Grad-CAM) and Local Interpretable Model-Agnostic Explanations (LIME) techniques, generating visual explanations during inference. Training spanned 10 epochs with early stopping to mitigate overfitting risks. SemiXAI-Net achieves a superior accuracy of 93%, precision of 94%, recall of 95%, and an F1-score of 94.5%. In comparison, traditional methods reach a maximum accuracy of 87%. The explainability score of SemiXAI-Net stands at 94%, significantly surpassing the traditional method’s score of 71%. Additionally, the framework demonstrates a generalization ability of 94% on unseen data, making it highly adaptable to new environments. These results highlight SemiXAI-Net’s potential for real-world applications, particularly in critical domains like autonomous driving, where both high performance and model transparency are essential.