Infrared remote sensing (IRS) ship detection faces challenges such as low resolution and environmental interference, with issues being particularly pronounced for small targets. This study proposes a lightweight architecture based on RT-DETR, termed RT-DETR-CST: A Cross-Channel Feature Attention Network (CFAN) is constructed, which achieves channel-weighted feature fusion via residual connections to suppress invalid background channels, addressing the problem of inter-channel information imbalance in infrared images and the suppression of small-target features by background noise. A Scale-Wise Feature Network (SWN) is developed, utilizing depthwise separable convolutions and stochastic depth for multi-scale feature extraction, where stochastic depth enhances the model’s robustness to small-target features. A Texture/Detail Capture Network (TCN) is built, achieving edge/detail capture through linear decomposition and low-cost channel fusion to solve the problems of target edge blurring and detail feature loss in infrared images caused by low signal-to-noise ratios. Experiments on the ISDD datasets show that RT-DETR-CST achieves an mAP0.5 metric of 89.4% (a 4.9% improvement over RT-DETR), reduces model size to 23.7 MB (a 41.5% reduction), and achieves an inference speed of 207.2 FPS. Ablation experiments validate the effectiveness of each module, demonstrating the model’s superior accuracy, lightweight design, and real-time performance in infrared ship remote sensing small-target detection. Furthermore, the generalization verification on the SSDD and SIRST datasets shows that the proposed model is effective in both infrared and SAR remote sensing small target detection.

The intelligent harvesting technology for jujube leaf branches presents a novel avenue for enhancing both the quantity and quality of jujube leaf tea, whereas the precise detection technology for jujube leaf branches emerges as a pivotal factor constraining its development. The precise identification and localization of jujube leaf branches using real-time object detection technology are crucial steps toward achieving intelligent harvesting. When integrated into real-world scenarios, issues such as the background noise introduced by tags, occlusions, and variations in jujube leaf morphology constrain the accuracy of detection and the precision of localization. To address these issues, we describe a jujube leaf branch object detection network based on YOLOv7. First, the Polarized Self-Attention module is embedded into the convolutional layer, and the Gather-Excite module is embedded into the concat layer to incorporate spatial information, thus achieving the suppression of irrelevant information such as background noise. Second, we incorporate implicit knowledge into the Efficient Decoupled Head and replace the original detection head, enhancing the network’s capability to extract deep features. Third, to address the issue of imbalanced jujube leaf samples, we employ Focal-EIoU as the bounding box loss function to expedite the regression prediction and enhance the localization accuracy of the model’s bounding boxes. Experiments show that the precision of our model is 85%, which is increased by 3.5% compared to that of YOLOv7-tiny. The mAP@0.5 value is 83.7%. Our model’s recognition rate, recall and mean average precision are superior to those of other models. Our method could provide technical support for yield estimation in the intelligent management of jujube orchards.