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.

In the task of classifying high-altitude flying objects, due to the limitations of the target flight altitude, there are issues such as insufficient contour information, low contrast, and fewer pixels in the target objects obtained through infrared detection technology, making it challenging to accurately classify them. In order to improve the classification performance and achieve the effective classification of the targets, this study proposes a high-altitude flying object classification algorithm based on radiation characteristic data. The target images are obtained through an infrared camera, and the radiation characteristics of the targets are measured using radiation characteristic measurement techniques. The classification is performed using an attention-based convolutional neural network (CNN) and gated recurrent unit (GRU) (referred to as ACGRU). In ACGRU, CNN-GRU and GRU-CNN networks are used to extract vectorized radiation characteristic data. The raw data are processed using Highway Network, and SoftMax is used for high-altitude flying object classification. The classification accuracy of ACGRU reaches 94.8%, and the F1 score reaches 93.9%. To verify the generalization performance of the model, comparative experiments and significance analysis were conducted with other algorithms on radiation characteristic datasets and 17 multidimensional time series datasets from UEA. The results show that the proposed ACGRU algorithm performs excellently in the task of high-altitude flying object classification based on radiation characteristics.