A training method for a weakly semi-supervised infrared small target detection model

By employing a weak semi-supervised approach and utilizing iterative updates of pixel-level mask labels and pseudo-labels, the training instability and crash issues in infrared weak target detection are resolved, thereby improving the network's generalization ability and detection performance.

CN118247606BActive Publication Date: 2026-05-26BEIHANG UNIV
2 Cites 0 Cited by

Patent Information

Application Number
CN202410568727.3
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2024-05-09
Publication Date
2026-05-26
Estimated Expiration
2044-05-09

AI Technical Summary

Technical Problem

Existing technologies suffer from instability in the early training stages and network crashes in infrared weak target detection, especially when using single-point labels versus fully supervised labels, where class imbalance is a serious problem.

Method used

We adopt a weak semi-supervised method, which performs one iteration of training on the CNN network using pixel-level mask labels. The weakly supervised labels are then updated multiple times based on the network output to gradually refine the pseudo-labels until the network converges. Finally, a second iteration of training is performed using the complete pixel-level mask labels and the evolved pseudo-labels.

Benefits of technology

It effectively avoids instability and network crashes in the early stages of training, improves the network's generalization ability, and achieves detection performance similar to that of fully supervised training with very little labeled data.

✦ Generated by Eureka AI based on patent content.
Patent Text Reader

Abstract

This invention discloses a training method for a weakly semi-supervised infrared weak target detection model, applied to a deep learning-based infrared weak target detection algorithm. The method includes the following steps: obtaining pixel-level mask labels and weakly supervised labels for an image set; performing one iteration of training on a CNN network using the pixel-level mask labels to obtain an initial detection model; during each iteration, updating the weakly supervised labels based on the output of the CNN network after a preset number of iterations; and performing a second iteration of training on the initial detection model using the updated weakly supervised labels and pixel-level mask labels until the network converges to obtain the final detection model. This method effectively avoids instability issues in the early stages of training and prevents network collapse when using both single-point labels and fully supervised labels simultaneously.
Need to check novelty before this filing date? Find Prior Art