Infrared small-target detection has widespread influences on anti-missile warning, precise weapon guidance, infrared stealth and anti-stealth, military reconnaissance, and other national defense fields. However, small targets are easily submerged in background clutter noise and have fewer pixels and shape features. Furthermore, random target positions and irregular motion can lead to target detection being carried out in the whole space–time domain. This could result in a large amount of calculation, and the accuracy and real-time performance are difficult to be guaranteed. Therefore, infrared small-target detection is still a challenging and far-reaching research hotspot. To solve the above problem, a novel multimodal feature fusion network (MFFN) is proposed, based on morphological characteristics, infrared radiation, and motion characteristics, which could compensate for the deficiency in the description of single modal characteristics of small targets and improve the recognition precision. Our innovations introduced in the paper are addressed in the following three aspects: Firstly, in the morphological domain, we propose a network with the skip-connected feature pyramid network (SCFPN) and dilated convolutional block attention module integrated with Resblock (DAMR) introduced to the backbone, which is designed to improve the feature extraction ability for infrared small targets. Secondly, in the radiation characteristic domain, we propose a prediction model of atmospheric transmittance based on deep neural networks (DNNs), which predicts the atmospheric transmittance effectively without being limited by the complex environment to improve the measurement accuracy of radiation characteristics. Finally, the dilated convolutional-network-based bidirectional encoder representation from a transformers (DC-BERT) structure combined with an attention mechanism is proposed for the feature extraction of radiation and motion characteristics. Finally, experiments on our self-established optoelectronic equipment detected dataset (OEDD) show that our method is superior to eight state-of-the-art algorithms in terms of the accuracy and robustness of infrared small-target detection. The comparative experimental results of four kinds of target sequences indicate that the average recognition rate P_avg is 92.64%, the mean average precision (mAP) is 92.01%, and the F1 score is 90.52%.

Addressing the formidable challenges in spatial infrared dim target detection, this paper introduces an advanced detection approach based on the refinement of the YOLOv8 algorithm. In contrast to the conventional YOLOv8, our method achieves remarkable improvements in detection accuracy through several novel strategies. Notably, by incorporating a deformable convolutional module into the YOLOv8 backbone network, our method effectively captures more intricate image features, laying a solid foundation for subsequent feature fusion and detection head predictions. Furthermore, a dedicated small target detection layer, built upon the original model, significantly enhances the model’s capability in recognizing infrared small targets, thereby boosting overall detection performance. Additionally, we utilize the WIoU-v3 as the localization regression loss function, effectively reducing sensitivity to positional errors and leveraging the advantages of multi-attention mechanisms. To enrich the quantity and quality of the spatial infrared dim target dataset, we employ image enhancement techniques to augment the original dataset. Extensive experiments demonstrate the exceptional performance of our method. Specifically, our approach achieves a precision of 95.6%, a recall rate of 94.7%, and a mean average precision (mAP) exceeding 97.4%, representing substantial improvements over the traditional YOLOv8 algorithm. Moreover, our detection speed reaches 59 frames/s, satisfying the requirements for real-time detection. This achievement not only validates the efficacy and superiority of our algorithm in spatial infrared dim target detection, but also offers novel insights and methodologies for research and applications in related fields, holding immense potential for future applications.

In the infrared small target images with complex backgrounds, there exist various interferences that share similar characteristics with the target (such as building edges). The accurate detection of small targets is crucial in applications involving infrared search and tracking. However, traditional detection methods based on small target feature detection in a single frame image may result in higher error rates due to insufficient features. Therefore, in this paper, we propose an infrared moving object detection method that integrates spatio-temporal information. To address the limitations of single-frame detection, we introduce a temporal sequence of images to suppress false alarms caused by single-frame detection through analyzing motion features within the sequence. Firstly, based on spatial feature detection, we propose a multi-scale layered contrast feature (MLCF) filtering for preliminary target extraction. Secondly, we utilize the spatio-temporal context (STC) as a feature to track the image sequence point by point, obtaining global motion features. Statistical characteristics are calculated to obtain motion vector data that correspond to abnormal motion, enabling the accurate localization of moving targets. Finally, by combining spatial and temporal features, we determine the precise positions of the targets. The effectiveness of our method is evaluated using a real infrared dataset. Through analysis of the experimental results, our approach demonstrates stronger background suppression capabilities and lower false alarm rates compared to other existing methods. Moreover, our detection rate is similar or even superior to these algorithms, providing further evidence of the efficacy of our algorithm.

With the continuous progress of remote sensing technology, the demand for means of detecting small targets in remote sensing images is escalating. The significance of detecting small targets in remote sensing images lies in enhancing the ability to identify small and elusive targets and the detection accuracy against complex backgrounds, holding significant application value in military reconnaissance, environmental monitoring, and disaster early-warning systems. Firstly, the minuteness of certain targets in relation to the entire image in which they occur, particularly when the camera is situated at a higher altitude, renders them difficult to detect. Secondly, the varying background and lighting conditions in remote sensing images further complicate the detection task. Conventional target detection methods are frequently incapable of addressing these complexities, resulting in a reduction in detection accuracy and an increase in false alarms. Hence, in this paper, we propose a lightweight remote-sensing image target detection network model, CBGS-YOLO, created by introducing the Ghost module to decrease the model parameters, applying the SPD-Conv module to optimize downsampling, and integrating the convolutional block attention module to enhance detection accuracy. The experimental outcomes demonstrate that CBGS-YOLO outperforms other models when applied to the DB_Licenta and USOD datasets, significantly enhancing detection performance for small targets. Compared with YOLOv9, this model can reduce the number of parameters from 7.10 M to 5.12 M, and the average precision (mAP) is effectively improved. The model strengthens the ability to identify small targets against complex backgrounds while maintaining lightweight properties and possesses remarkable application prospects and practical value.