Infrared small-target detection plays a critical role in military surveillance，security monitoring，remote sensing，and unmanned aerial vehicle （UAV） operations. Accurate detection of such targets is essential for threat assessment，object tracking，and decision-making in complex operational scenarios. In real-world environments，infrared targets are often extremely small in size，have low contrast against complex backgrounds，and are easily affected by noise. These characteristics make the extraction of discriminative features that can effectively separate targets from surrounding clutter challenging. Moreover，differences in semantic content across feature layers impede the effective fusion of shallow detailed features with deep semantic features，further limiting the accuracy and robustness of detection. Designing efficient，accurate，and robust methods for infrared small-target detection under complex backgrounds remains a critical research problem in the field of computer vision and defense-related applications.

To address these challenges，we propose a novel multibranch perception and cross-layer semantic fusion network （MPCF-Net） for infrared small-target detection. The network is built on an encoder-decoder architecture designed to capture both local and global contextual information while mitigating the computational cost. In the encoder，we introduce a multibranch perception fusion attention module （MPFM） that simultaneously processes local，global，and serial convolutional branches to extract multiscale features. The MPFM module is combined with local-global guided attention （LGGA），which integrates local spatial features and global semantic information to enhance the target response，and global channel spatial attention （GCSA），which models channel dependencies and spatial relationships to improve feature representation quality. A spatial-channel cross transformer block （SCTB） replaces conventional skip connections to effectively bridge semantic gaps between shallow and deep layers. SCTB models cross-layer context dependencies，allowing the network to capture hierarchical feature correlations while alleviating semantic differences between layers. This module ensures that shallow detailed and deep semantic features are adequately fused，improving the overall discriminative power of the network. In the decoder，depthwise separable convolution is employed to reduce computational complexity without sacrificing representational capability. Moreover，a light gradient gate （LGG） guided by Sobel gradients is introduced at the output stage to enhance small-target edge details and reinforce feature localization precision. By combining attention mechanisms with multibranch and cross-layer strategies，MPCF-Net can robustly detect infrared small targets in challenging and cluttered backgrounds.

The effectiveness of MPCF-Net is evaluated on three public infrared small-target datasets，namely，SIRST，IRSTD，and NUDT-SIRST，which contain diverse scenes with varying target sizes，shapes，and noise levels. Quantitative results demonstrate that MPCF-Net achieves intersection over union （IoU） scores of 80.12%，66.28%，and 84.26% and normalized intersection over union （nIoU） scores of 78.23%，64.58%，and 86.48% on SIRST，IRSTD，and NUDT-SIRST datasets，respectively. In terms of detection performance，MPCF-Net attains a high probability of detection of 99.88%，94.23%，and 98.21% while maintaining extremely low false alarm rates of 1.12 × 10^-6，4.39 × 10^-6，and 14.57 × 10^-6 on SIRST，IRSTD，and NUDT-SIRST datasets，respectively. We also analyze the effectiveness of individual components. Ablation studies reveal that the MPFM module substantially enhances multiscale feature extraction，LGGA improves the network’s sensitivity to target regions，and GCSA strengthens feature representation by emphasizing spatial-channel relationships. The SCTB module demonstrates clear advantages in reducing semantic conflicts between layers，leading to coherent and accurate feature fusion. LGG ensures that edge details are preserved and small targets are precisely localized，which is crucial for applications where missed detection could result in operational failure.

MPCF-Net effectively combines multibranch perception，attention mechanisms，and cross-layer semantic fusion to enhance discriminative feature extraction and hierarchical context modeling for infrared small targets. The network demonstrates high accuracy，robustness，and low false alarm rates in complex scenes. Its design provides a practical solution for real-world surveillance，military monitoring，and UAV-based remote sensing applications. Future work may explore adaptive integration with other sensor modalities and real-time deployment strategies to further enhance operational effectiveness.