This invention relates to the field of
computer vision, specifically to a passive domain adaptive target detection method and electronic device based on pseudo-
label confidence feedback, comprising the following steps: S1, acquiring a pre-trained source
domain model and unlabeled target domain data; S2, constructing a mean-teacher self-training architecture; S3, generating pseudo-labels and calculating confidence feedback signals; S4, implementing an adaptive dynamic mean-teacher update strategy. This invention, by introducing a pseudo-
label confidence feedback mechanism, enables the model to have adaptive adjustment capabilities, effectively suppressing the adverse effects of low-quality pseudo-labels on model training; it can dynamically reduce the decay rate in high-quality pseudo-
label scenarios, improving cross-domain
transfer efficiency and detection accuracy; this invention effectively alleviates the "confirmation bias" problem in passive domain adaptive tasks, significantly improving the model's training stability,
noise resistance, and cross-domain target
detection performance in the target domain, possessing good application value and promising prospects for promotion.