In recent years, the development of deep learning has brought great convenience to the work of target detection, semantic segmentation, and object recognition. In the field of infrared weak small target detection (e.g., surveillance and reconnaissance), it is not only necessary to accurately detect targets but also to perform precise segmentation and sub-pixel-level centroid localization for infrared small targets with low signal-to-noise ratio and weak texture information. To address these issues, we propose UCDnet (Double U-shaped Segmentation Network Cascade Centroid Map Prediction for Infrared Weak Small Target Detection) in this paper, which completes “end-to-end” training and prediction by cascading the centroid localization subnet with the semantic segmentation subnet. We propose the novel double U-shaped feature extraction network for point target fine segmentation. We propose the concept and method of centroid map prediction for point target localization and design the corresponding Com loss function, together with a new centroid localization evaluation metrics. The experiments show that ours achieves target detection, semantic segmentation, and sub-pixel-level centroid localization. When the target signal-to-noise ratio is greater than 0.4, the IoU of our semantic segmentation results can reach 0.9186, and the average centroid localization precision can reach 0.3371 pixels. On our simulated dataset of infrared weak small targets, the algorithm we proposed performs better than existing state-of-the-art networks in terms of semantic segmentation and centroid localization.

In applications such as aerial object interception and ballistic estimation, it is crucial to precisely detect the centroid position of the target rather than to merely identify the position of the target bounding box or segment all pixels belonging to the target. Due to the typically long distances between targets and imaging devices in such scenarios, targets often exhibit a low contrast and appear as dim, obscure shapes in infrared images, which represents a challenge for human observation. To rapidly and accurately detect small targets, this paper proposes a lightweight, end-to-end detection network for small infrared targets. Unlike existing methods, the input of this network is five consecutive images after background matching. This design significantly improves the network’s ability to extract target motion features and effectively reduces the interference of static backgrounds. The network mainly consists of a local feature aggregation module (LFAM), which uses multiple-sized convolution kernels to capture multi-scale features in parallel and integrates multiple spatial attention mechanisms to achieve accurate feature fusion and effective background suppression, thereby enhancing the ability to detect small targets. To improve the accuracy of predicted target centroids, a centroid correction algorithm is designed. In summary, this paper presents a lightweight centroid detection network based on background matching for weak, small infrared targets. The experimental results show that, compared to directly inputting a sequence of images into the neural network, inputting a sequence of images processed by background matching can increase the detection rate by 9.88%. Using the centroid correction algorithm proposed in this paper can therefore improve the centroid localization accuracy by 0.0134.

Recent advancements in the unmanned aerial vehicle remote sensing field have highlighted the effectiveness of infrared sensors in detecting and tracking time-sensitive ground targets, particularly within the domain of early warning and surveillance. However, the limitations inherent in airborne infrared platforms can lead to irregular imaging and inadequate textural features. This study presents a multi-object tracking system specifically designed for weak-textured infrared targets, aimed at enhancing detection accuracy and tracking stability. Initially, improvements are made to the YOLOv10 model through the incorporation of modules such as DSA, c2f_fasterblock, and NMSFree, which collectively enhance detection accuracy and robustness for weak-textured targets. Subsequently, the detection results are employed in conjunction with GM-PHD tracking, enabling rapid and stable target tracking. The proposed methodology demonstrates a 2.3% improvement in detection accuracy and a 3.8% increase in recall when assessed using publicly available infrared tracking datasets. Notably, the key tracking metric, MOTA, achieves a value of 90.7%, while the IDF1 score reaches 94.6%. The findings from the experiments indicate that the proposed algorithm surpasses current methodologies regarding effectiveness, accuracy, and robustness in the context of infrared multi-target tracking tasks, thereby meeting the requirements associated with airborne infrared target tracking tasks.