Infrared small target detection (ISTD) means distinguishing small and faint targets from IR images. Small targets typically span only a handful of pixels, lacking distinct texture and clear structural details. For the past few years, deep learning has made big strides in the field of ISTD. Yet, a persistent challenge remains: the lack of high-level semantic information may cause the disappearance of small target features in the network’s deep layers, ultimately impairing detection accuracy. To tackle this problem, we introduce WT-HMFF, an innovative network architecture that combines the Wavelet Transform Convolution (WTConv) module with the Hierarchical Multi-Scale Feature Fusion (HMFF) module to enhance the ISTD algorithm’s performance. WTConv expands the receptive field through wavelet convolution, effectively capturing global contextual information while preserving target shape characteristics. The HMFF module enables the efficient fusion of shallow and deep features, maintaining the high resolution of deep feature maps and preventing the disappearance of small target features. We have tested it out on public datasets, SIRST and IRSTD-1k, and validated the superiority and robustness of WT-HMFF compared to other methods.

The identification of tiny objects via single-frame infrared is a significant challenge in computer vision, primarily due to large variances in target dimensions, overcrowded backgrounds, suboptimal signal-to-noise ratios, the propensity of bounding box regression to vary with target size, and potential partial occlusion scenarios. Addressing these challenges, we propose a sturdy network for enhancing features in the infrared detection of small targets utilizing multi-scale attention. In particular, the introduction of the Iterative Attentional Feature Fusion (iAFF) module at the detection network’s neck aims to tackle the issue of minor target features being overshadowed in the process of cross-scale feature fusion. Additionally, we present the Occlusion-Aware Attention Module (OAAM), which demonstrates greater tolerance for target localization errors in regions where local features are missing due to partial occlusion. By combining the scale and spatial attention mechanisms of the Dynamic Head, our approach adaptively learns the relative importance of different semantic layers. Furthermore, the integration of Normalized Wasserstein–Gaussian Distance (NWD) aims to tackle the convergence issues associated with the increased sensitivity of bounding box regression in identifying minor infrared targets. For assessing our technique’s efficiency, we present a novel benchmark dataset, IRMT-UAV, noted for its considerable discrepancies in target size, intricate backdrops, and substantial variations in the signal-to-noise ratio. The outcomes of our experiments using the public IRSTD-1k dataset and the internally developed IRMT-UAV dataset reveal that our technique surpasses cutting-edge (SOTA) methods, with mAP50 enhancements of 1.4% and 4.9%, respectively, thus proving our method’s efficiency and sturdiness.

The query propagation paradigm provides a unified theoretical framework for end-to-end multi-object tracking, yet it still faces challenges in complex scenarios involving multi-scale variations, dense interactions, and trajectory fragmentation, including insufficient query initialization quality, imprecise feature alignment, and difficult identity recovery. Building upon MOTRv2, this paper proposes three core improvements. First, we design a geometric prior injection strategy based on sine–cosine encoding, which explicitly encodes target location and scale information into detection queries, providing high-quality initialization for tracking queries. Second, we propose a width–height-modulated deformable attention mechanism that dynamically adjusts the sampling range of deformable convolution according to target size, enabling fine-grained feature matching for multi-scale targets. Third, we construct a motion-direction-consistency-based trajectory re-association module that leverages motion continuity to efficiently recover lost trajectories without introducing additional appearance models. Furthermore, we introduce a progressive joint training strategy that optimizes detection and tracking modules in stages, effectively mitigating gradient competition in multi-task learning. Extensive quantitative and qualitative experiments on the BEE24, UAVSwarm, and VTMOT infrared datasets validate the effectiveness of the proposed method. On the UAVSwarm dataset, our method achieves state-of-the-art performance with 52.4% HOTA, 72.1% MOTA, and only 51 identity switches. Ablation studies further reveal the synergistic enhancement mechanism among the proposed modules.