The detection of small targets in infrared imagery captured by unmanned aerial vehicles (UAVs) is critical for surveillance and monitoring applications. However, this task is challenged by the small target size, low signal-to-noise ratio, and the limited computational resources of UAV platforms. To address these issues, this paper proposes IFD-YOLO, a novel lightweight detector based on YOLOv11n, specifically designed for onboard infrared sensing systems. Our framework introduces three key improvements. First, a RepViT backbone enhances both global and local feature extraction. Second, a C3k2-DyGhost module performs dynamic and efficient feature fusion. Third, an Adaptive Fusion-IoU (AF-IoU) loss improves bounding-box regression accuracy for small targets. Extensive experiments on the HIT-UAV and IRSTD-1k datasets demonstrate that IFD-YOLO achieves a superior balance between accuracy and efficiency. Compared to YOLOv11n, our model improves mAP@50 and mAP@50:95 by 4.9% and 3.1%, respectively, while simultaneously reducing the number of parameters and GFLOPs by 23% and 21%. These results validate the strong potential of IFD-YOLO for real-time infrared sensing tasks on resource-constrained UAV platforms.

The fatigue driving detection process faces issues such as a large number of parameters, low accuracy and insufficient continuous detection. To address these, this paper proposes a method combining enhanced YOLOv5s and DeepSort for fatigue driving detection. First, the improved Mobilenet_ECA lightweight backbone is introduced to reconstruct the YOLOv5s backbone part. Then, the CSPDarknet53(C3) module in the neck network is integrated with the Triplet Attention Module (TAM) to enhance the fusion of contextual information and improve the network’s facial feature extraction capability. In addition, FocalEIOU Loss is used to optimize the problem of large errors in Complete Intersection over Union (CIoU) Loss prediction box regression calculations. Next, the DeepSort facial feature tracking algorithm is combined for continuous classification tracking to optimize the problem of driver facial information loss. Finally, the facial feature detection results are combined with the number of consecutive eyeclosed frames, the number of consecutive yawning frames, and the Percentage of Eyelid Closure over the Pupil over Time (PERCLOS) score to build a fatigue driving detection model. The experimental results show that compared with the baseline model YOLOv5s, the algorithm proposed in this article has improved mAP@0.5 and P by 1% and 1.8%, Params decreased by 56.3%, FLOPs decreased by 63.2%, and the model size is only 6.4 MB. The final recognition rate of fatigue and nonfatigue driving was as high as 97.4%. It is verified that the algorithm in this paper can maintain high detection accuracy while being lightweight, and can effectively identify driver states, providing strong support for the deployment of vehicle edge devices.

Pedestrian detection faces significant performance degradation challenges in nighttime visible light environments due to degraded target features, background noise interference, and the coexistence of multi-scale targets. To address this issue, this paper proposes a BNE-DETR model based on an improved RT-DETR. First, we incorporate the lightweight backbone network CSPDarknet and design a Single-head Self-attention with EPGO and Convolutional Gated Linear Unit (SECG) module to replace the bottleneck layer in the original C2f component. By integrating single-head self-attention, the Efficient Prompt Guide Operator (EPGO) dynamic K-selection mechanism, and convolutional gated linear units, it effectively enhances the model’s feature representation capability under low-light conditions. Second, the AIFI-SEFN module, which combines Attention-driven Intra-scale Feature Interaction (AIFI) with a Spatially Enhanced Feedforward Network (SEFN), is constructed to strengthen the extraction of weak details and the fusion of contextual information. Finally, the Mixed Aggregation Network with Star Blocks (MANStar) module utilizes large-kernel convolutions and multi-branch star structures to enhance the representation and fusion of multi-scale pedestrian features. Experiments on the LLVIP dataset demonstrate that our model achieves 1.9%, 2.5%, and 1.9% improvements in Precision, Recall, and mAP50, respectively, compared to RT-DETR-R18, while maintaining low computational complexity (48.7 GFLOPs) and reducing parameters by 20.2%. Cross-dataset experiments further validate the method’s robust performance and generalization capabilities in nighttime pedestrian detection tasks.

Detecting small infrared targets in vehicle-mounted systems remains challenging due to weak thermal radiation, cross-scale feature loss, and dynamic background interference. To address these issues, this paper proposes MSLCP-DETR, an enhanced RT-DETR-based framework that integrates multi-scale linear attention and sparse fusion mechanisms. The model introduces three novel components: a Multi-Scale Linear Attention Encoder (MSLA-AIFI), which combines multi-branch depth-wise convolution with linear attention to efficiently capture cross-scale features while reducing computational complexity; a Cross-Scale Small Object Feature Optimization module (CSOFO), which enhances the localization of small targets in dense scenes through spatial rearrangement and dynamic modeling; and a Pyramid Sparse Transformer (PST), which replaces traditional dense fusion with a dual-branch sparse attention mechanism to improve both accuracy and real-time performance. Extensive experiments on the M3FD and FLIR datasets demonstrate that MSLCP-DETR achieves an excellent balance between accuracy and efficiency, with its precision, mAP@50, and mAP@50:95 reaching 90.3%, 79.5%, and 86.0%, respectively. Ablation studies and visual analysis further validate the effectiveness of the proposed modules and the overall design strategy.

Integrating infrared and visible-light images facilitates a more comprehensive understanding of scenes by amalgamating dual-sensor data derived from identical environments. Traditional CNN-based fusion techniques are predominantly confined to local feature emphasis due to their inherently limited receptive fields. Conversely, Transformer-based models tend to prioritize global information, which can lead to a deficiency in feature diversity and detail retention. Furthermore, methods reliant on single-scale feature extraction are inadequate for capturing extensive scene information. To address these limitations, this study presents GLMAFuse, an innovative dual-stream encoder–decoder network, which utilizes a multi-scale attention mechanism to harmoniously integrate global and local features. This framework is designed to maximize the extraction of multi-scale features from source images while effectively synthesizing local and global information across all layers. We introduce the global-aware and local embedding (GALE) module to adeptly capture and merge global structural attributes and localized details from infrared and visible imagery via a parallel dual-branch architecture. Additionally, the multi-scale attention fusion (MSAF) module is engineered to optimize attention weights at the channel level, facilitating an enhanced synergy between high-frequency edge details and global backgrounds. This promotes effective interaction and fusion of dual-modal features. Extensive evaluations using standard datasets demonstrate that GLMAFuse surpasses the existing leading methods in both qualitative and quantitative assessments, highlighting its superior capability in infrared and visible image fusion. On the TNO and MSRS datasets, our method achieves outstanding performance across multiple metrics, including EN (7.15, 6.75), SD (46.72, 47.55), SF (12.79, 12.56), MI (2.21, 3.22), SCD (1.75, 1.80), VIF (0.79, 1.08), Qbaf (0.58, 0.71), and SSIM (0.99, 1.00). These results underscore its exceptional proficiency in infrared and visible image fusion.