Targets in infrared images typically lack distinct texture and color features, posing significant challenges for infrared small target detection. Although existing methods have made some progress, they still have limitations in terms of interpretability and are unable to provide sufficient theoretical grounds to explain their effectiveness. Moreover, these methods generally overlook the temperature information in the imaging process, failing to fully exploit the subtle thermal differences in infrared images, which consequently limits detection accuracy. To this end, we propose a novel temperature-aware Transformer network (TATransNet). By introducing temperature information, it allows the model to be more focused on the target area. Specifically, inspired by the temperature inversion technique in remote sensing, we propose a learnable temperature inversion attention module (TIAM), which simulates the inverse process of infrared imaging to perceive the temperature distribution across feature maps of different scales, thereby enhancing the distinguishability between foreground and background. We further integrate TIAM with Swin Transformer to construct a Transformer-CNN hybrid encoder unit, termed temperature-aware Transformer block (TATB). By stacking multiple TATBs, both global context and local temperature features can be captured simultaneously, thereby strengthening the representation of dim and small targets effectively. Experimental results on five benchmark datasets, i.e., Small-ExtIRShip, Small-SSDD, NUAA-SIRST, IRSTD-1K, and IHAST demonstrate that our method not only significantly improves detection accuracy and reduces false alarm rates, but also demonstrates strong generalization capability, enabling effective transfer to other approaches with different types of encoders.

Abstract The safe operation of gas pipelines is crucial for the safety of residents’ lives and property. However, accurately detecting defects within these gas pipelines is a challenging task. To improve the accuracy of defect detection in pipeline DR images with small sample sizes, we propose an enhanced Faster RCNN model based on a Smooth Variational Autoencoder and Enhanced Detection Head (S-EDH-Faster RCNN). This model leverages a smooth variational autoencoder to reconstruct features and enhances classification scores through an improved detection head, thereby boosting overall detection accuracy. In detail, to address the issue of scarce training samples for new categories, we design a smooth variational autoencoder to reconstruct features that better fit the distribution of training data. Furthermore, to refine classification precision, we present an enhanced detection head that incorporates a convolutional block attention-based center point classification calibration module, which strengthens classification-related portions of the RoI features and adjusts classification scores accordingly. Finally, to effectively learn characteristics of novel class samples, we introduce an adaptive fine-tuning method that adaptively updates key convolutional kernels during the fine-tuning stage, enabling the model to generalize better to novel classes. Experimental results demonstrate that our approach achieves superior detection performance over state-of-the-art models on both the home-made PIP-DET dataset and the publicly available NEU-DET dataset, demonstrating its effectiveness.

The working conditions of gas pipelines directly impact urban populations and factory operations. However, accurate and rapid detection of gas pipeline defects is challenging. To improve the accuracy of gas pipeline defect detection , we propose an improved RefineDet (Im-RefineDet) for gas pipeline defect detection, in which the improvement is carried out from the backbone network and the detection head. Specifically, to extract richer features, we design an improved CrossFormer as the backbone network. It first adopts a small convolutional cross-scale embedding layer to perform convolution, and then uses stripe window self-attention in vertical and horizontal directions in sequence to extract different features. In the detection head, we present a Double Attention Decouple Head (DADH) for classification and localization, enabling the model to perform independent optimization of the two branches. DADH employs spatial-aware and scale-aware attention to acquire multi-scale features, subsequently conducting classification and localization separately to derive final detection outcomes. Additionally, we apply channel pruning to the model to achieve a lightweight design, improving computational efficiency without significantly compromising detection performance. Experimental results, derived from an in-house developed gas pipeline defect image dataset, as well as two publicly available datasets — the NEU-DET dataset and the PCB dataset — demonstrate the effectiveness of the proposed Im-RefineDet. These results highlight its superior performance compared to state-of-the-art methods, further validating its robustness and adaptability across diverse scenarios. Specifically, the model achieves the mean Average Precision (mAP) of 92.6% on the gas pipeline defect image dataset, 77.8% on the NEU-DET dataset, and 99.2% on the PCB defect detection dataset.

Object detection in infrared images poses a considerable challenge due to its small-scale targets, low contrast and poor signal-to-clutter ratio, often resulting in a high false alarm rate. To improve the detection accuracy on infrared small targets, we introduce Light-SGMTLM, a lightweight and saliency-guided multi-task learning model. This model integrates saliency detection into the YOLOv5x framework through a parallel multi-task learning structure and employs a joint loss function during training. Such integration significantly alleviates the impact of complex backgrounds and improves the precision of small target localization. Moreover, we have developed a streamlined module, termed SIWD, to create a more agile backbone, which establishes an optimal balance between precision and efficiency, making the model more suitable for situations with limited computational resources. Comprehensive comparative experiments were conducted on six infrared small target datasets, namely, Small-ExtIRShip, Small-SSDD, IHAST, NUAA-SIRST, IRSTD-1k, and IRDST, and we assessed the model’s performance against ten leading target detection models, such as YOLOv7, YOLOv8, DINO, and Relation-DETR. The findings reveal that our method’s unique joint learning architecture, combining saliency and object detection tasks, significantly improves accuracy for infrared small target detection. Notably, it achieved impressive mean average precision (mAP) values of 92.60% and 75.71% on the NUAA-SIRST and IRSTD-1k datasets, respectively.

Infrared (IR) small target detection is a challenging task due to the low contrast and low signal-to-noise ratio, generally yields high false alarm rates. To improve the performance of IR small target detection, we propose a saliency guided multi-task leaning model (SGMTLM). The model consists of two parts: feature fusion and saliency detection. The feature fusion module is to integrate shallow information and deep semantic information of small targets. The saliency detection module is used to guide the Feature Pyramid Networks (FPN) to focus on the small target area. It can effectively suppress the non-target information while enhancing the small target information. Finally, experimental results on two datasets Small-ExtIRShip and Small-SSDD demonstrated that, with the help of saliency detection, the proposed method can effectively improve the accuracy of IR small target detection, achieving 95.78% and 98.70% mAP on the two datasets, respectively.

Ship target segmentation in infrared scenes has always been a hot topic, since it is an important basis and prerequisite for infrared-guided weapons to reliably capture and recognize ship targets in the sea level background. However, given the small target and fuzzy boundary characteristics of infrared ship images, obtaining accurate pixel-level labels for them is hardly achievable, which brings difficulty to train segmentation networks. To improve the segmentation accuracy of infrared ship images, we propose a two-stage domain adaptation method for infrared ship target segmentation, where the segmentation model is trained using visible ship images with clear target boundaries. In this case, the source domain is the labeled visible ship images, while the target domain is the unlabeled infrared ship images. Specifically, in the first stage, we use an image style transfer network to convert the infrared ship images into those with visible light style, so that the visual disparity between the two domain images can be reduced. Next, the visible, infrared and converted infrared images are input into the Deeplab-v2 segmentation network for training, thereby obtaining the initial network weights. At this time, random attention modules are added separately to the low- and high-level spaces of Deeplab-v2, in order to improve its feature extraction capability. In the second stage, we mix the visible and infrared images through region mixing to acquire the mixed domain images, as well as their corresponding labels. Subsequently, Deeplab-v2 is further trained using the mixed domain images to attain better segmentation accuracy. Experimental results on both the home-made visible-infrared ship image dataset and the public infrared image dataset are superior to those existing mainstream methods, demonstrating its effectiveness.

Infrared ship segmentation is extensively applied in military fields. Due to noise and intensity inhomogeneity, the segmentation of infrared ship is a challenging task. The fuzzy c-means (FCM) clustering algorithm is widely used in image segmentation. However, traditional FCM is sensitive to noise and unable to obtain desirable segmentation results for infrared ship images. In this article, a novel probability induced intuitionistic FCM clustering algorithm is proposed to address the problem. First, the target probability information is incorporated into intuitionistic FCM to induce and refine membership which is affected by interferences. Second, by making use of neighborhood information in the form of a regularization term, the proposed method could suppress intensity inhomogeneity as well as maintain image details. Experimental results demonstrate that the proposed method could achieve better results than 12 other comparing algorithms for infrared ship segmentation.

In this letter, we construct MaRine ShiP (MRSP-13), a novel dataset containing 37,161 ship target images belonging to 13 classes with bounding box annotation, and among them there are 3051 images labeled with pixel-level annotation. This dataset equips us with the capability to conduct baseline experiments on maritime target classification, detection and segmentation. We propose a cross-layer multi-task CNN model for maritime target detection, which can simultaneously solve ship target detection, classification, and segmentation. Experimental results have demonstrated the efficiency of the MRSP-13 dataset to be used for maritime target analysis. In addition, the results validate the fact that by adopting the strategies of feature sharing, joint learning, and cross-layer connections, the proposed model achieves superior performance with less annotations. We believe that our MRSP-13 dataset and corresponding baseline experiments will lay down the foundation for further research in maritime target processing.

Feature clustering is a powerful technique for dimensionality reduction. However, existing approaches require the number of clusters to be given in advance or controlled by parameters. In this paper, by combining with affinity propagation (AP), we propose a new feature clustering (FC) algorithm, ca lled APFC, for dimensionality reduction. For a given training dataset, the original features automatically form a bunch of clusters by AP. A new feature can then be extracted from each cluster in three different ways for reducing the dimensionality of the original data. APFC requires no provision of the number of clusters (or extracted features) beforehand. Moreover, it avoids computing the eigenvalues and eigenvectors of covariance matrix which is often necessary in many feature extraction methods. In order to demonstrate the effectiveness and efficiency of APFC, extensive experiments are conducted to compare it with three well-established dimensionality reduction methods on 14 UCI datasets in terms of classification accuracy and computational time.

Segmentation of infrared (IR) ship images is always a challenging task, because of the intensity inhomogeneity and noise. The fuzzy C-means (FCM) clustering is a classical method widely used in image segmentation. However, it has some shortcomings, like not considering the spatial information or being sensitive to noise. In this paper, an improved FCM method based on the spatial information is proposed for IR ship target segmentation. The improvements include two parts: 1) adding the nonlocal spatial information based on the ship target and 2) using the spatial shape information of the contour of the ship target to refine the local spatial constraint by Markov random field. In addition, the results of K -means are used to initialize the improved FCM method. Experimental results show that the improved method is effective and performs better than the existing methods, including the existing FCM methods, for segmentation of the IR ship images.

Segmentation of infrared (IR) ship images is always a challenging task, because of the intensity inhomogeneity and noise. The Fuzzy C-Means (FCM) clustering is a classical method widely used in IR ship image segmentation. However, it has some shortcomings, like not considering the spatial information or being sensitive to noise. In this paper, an improved FCM algorithm based on the spatial information is proposed. The improvements include two parts: (1) adding the non-local spatial information based on the ship target; (2) using the spatial shape information of the contour of the ship target to refine the local spatial constraint by Markov Random Field (MRF). A preprocessing procedure and a target selection method are also used to further improve the performance of the segmentation result. Experimental results show that our method is very effective and performs better than the conventional FCM methods in segmentation of the infrared ship images.