Qi Ma

The success of deep learning methods heavily relies on the availability of adequate samples. However, in the task of infrared small target detection (ISTD), the lack of high-quality training samples is a challenging problem due to the confidentiality of the application field and the difficulty of labeling. This limitation often leads to suboptimal detection performance of convolutional neural networks (CNNs). To address this challenge, we propose an adaptive labeling strategy and an ISTD network called the multi-criteria decision network (MCDNet) to achieve higher-quality sample labeling and more accurate detection results. In the adaptive labeling strategy, we propose a second-order differential autocorrelation method to determine the size of fuzzy edge targets accurately. In addition, we introduce local backgrounds to enhance the saliency information in the labels and improve the richness and contrast of training label content. To obtain accurate and robust detection results with limited target feature information, we design MCDNet. In particular, we propose a multi-criteria decision method that can combine the CNN decisions and the infrared small target prior saliency decisions through weighted fusion, and set decision weights based on the importance of different decision criteria in the decision-making process. This method can integrate the advantages of both the CNN decisions and the prior saliency decisions, avoid the one-sidedness of a single criterion, and improve the reliability and stability of the decision-making. The experimental results indicate that our method has a higher accuracy compared to other contrastive methods.

Infrared small target detection plays a crucial role in various domains like early warning, national defense, and monitoring. Although existing detection methods have achieved some good results, they only rely on the original size and information of small targets for detection. And faced with challenges such as the small size and obscure feature information of small targets. To overcome these limitations, this paper introduces a coarse-to-fine detection network named MSMA-Net. This network initially determines the rough location of targets through a coarse preliminary screening, aiming to reduce false alarms and improve computational efficiency. Simultaneously, to improve the discriminability of the features and enhance the spatial details and resolution of the targets, the network utilizes multi-scale super-resolution to transform low-resolution feature maps into high-resolution representations, gradually refining and strengthening the feature representation. Finally, the network employs a multi-level feature fusion attention mechanism to facilitate effective information transmission and fusion in multi-scale and multi-level feature representations. This attention mechanism enhances the accuracy as well as robustness of object detection, ultimately obtaining accurate detection results. Extensive experimental results demonstrate that compared to existing detection methods, our approach can effectively suppress false alarms and get better performance even when the target has a small size and obscure feature information.

With the continuous development of infrared technology, the application of infrared imagery is increasingly widespread. Nonetheless, infrared imagery suffers from low contrast and high noise characteristics, making it challenging to detect and recognize infrared small targets. Frequently, existing deep learning-based infrared target detection methods solely employ spatial information, neglecting frequency domain information, thereby creating susceptibility to background clutter interference and low detection accuracy in complex scenes. Here, a deep learning-based method is proposed that combines frequency domain clutter suppression and spatial feature extraction to detect infrared small targets. Firstly, the spatial domain infrared image is transformed into the frequency domain using block discrete cosine transform (DCT). Secondly, the frequency domain information is filtered using an attention mechanism to adaptively enhance the targets and suppress the background clutter. Finally, the compressed attention map is transformed back to the spatial domain and inputted into a lightweight neural network for spatial feature extraction and fusion to obtain detection results. This approach enhances detection accuracy, and reduces computational complexity and storage space, thereby making it practical and deployable. Experimental outcomes demonstrate strong detection capabilities on public datasets, and significant advantages over traditional and existing deep learning methods.

A new deep learning method based on the target gray-level distribution constraint mechanism model is proposed to solve the infrared dim small target detection problem in the complex environment. First, to solve the uneven distribution of positive and negative samples, the designed smoothness operator is used to suppress the background and enhancement target by measuring the difference in their features in 1D and 2D gradient. Second, an infrared dim small target detection network based on dense feature fusion, namely the DFFIR-net network, is proposed. The DFFIR-net enhances the feature expression of dim small targets by integrating the original features and the smoothness features of gray-level gradient. Also, the DFFIR-net alleviates the problem of sparse feature extraction. Finally, a multiscale 2D Gaussian label generation strategy is proposed. This strategy is critical in supervising the training of DFFIR-net in multi-dimensional Gaussian space, improving the feature exploration ability of the network and detection performance under small training samples. The experimental results show that compared with the existing advanced detection methods, the proposed method has higher accuracy and lower false alarm rates in various complex scenes.