Meta-learning based infrared ship object detection model for generalization to unknown domains

Infrared images exhibit considerable variations in probability distributions, stemming from the utilization of distinct infrared sensors and the influence of diverse environmental conditions. The variations pose great challenges for deep learning models to detect ship objects and adapt to unseen maritime environments. To address the domain shift problem, we propose an end-to-end infrared ship object detection model based on meta-learning neural network to improve domain adaptation for target domain where data is not available at training phase. Different from existing domain generalization methods, the novelty of our model lies in the effective exploitation of meta-learning and domain adaptation, ensuring that the extracted domain-independent features are meaningful and domain-invariant at the semantic level. Firstly, a double gradient-based meta-learning algorithm is designed to solve the common optimal descent direction between different domains through two gradient updates in the inner and outer loops. The algorithm enables extraction of domain-invariant features from the pseudo-source and pseudo-target domain data. Secondly, a domain discriminator with dynamic-weighted gradient reversal layer (DWGRL) is designed to accurately classify domain-invariant features and provide additional global supervision information. Finally, a multi-scale feature aggregation method is proposed to improve the extraction of multi-scale domain-invariant features. It can effectively fuse local features at different scales and global features of targets. Extensive experimental results conducted in real nighttime water surface scenes demonstrate that the proposed model achieves very high detection accuracy on target domain data, even no target domain data was used during the training phase. Compared to the existing methods, our method not only improves the detection accuracy of infrared ships by 18%, but also exhibits the smallest standard deviation with a value of 0.93, indicating its superior generalization performance

Flying small target detection in IR images based on adaptive toggle operator

Automatic detection and tracking of a small target in infrared (IR) images are of great importance. Toggle operator (TO) is the newest class of non‐linear operator morphology that has been widely used in detection and tracking the target in IR images. The most important problem in improving the efficiency of the TO is to use structural elements (SEs) in accordance with signal‐to‐clutter ratio (SCR) of each image. Generally, the clutters and targets are different in case of each image; therefore, for images with different SCRs, using SEs with fixed pixels and dimensions cannot lead to successful target detection. In this study, a new method is presented based on genetic algorithm to achieve adaptive SE for target detection in IR images. In this method, by designing the SE in accordance with the characteristics of each image, a large amount of background clutter and noise is suppressed and the contrast between target and background is increased. The results of a large set of real IR images including moving targets show that the proposed algorithm is effective in target detection. In the proposed method, the contrast between the target and background clutter is greatly increased while maintaining a low false alarm rate