High-precision detection of rice targets in precision agriculture is crucial for yield assessment and field management. However, existing models still face challenges, such as high rates of missed detections and insufficient localization accuracy, particularly when dealing with small targets and dynamic changes in scale and morphology. This paper proposes an accurate rice detection model for UAV images based on Adaptive Aware Dynamic Convolution, named Adaptive Dynamic Convolution YOLO (ADC-YOLO), and designs the Adaptive Aware Dynamic Convolution Block (ADCB). The ADCB employs a “Morphological Parameterization Subnetwork” to learn pixel-specific kernel shapes and a “Spatial Modulation Subnetwork” to precisely adjust sampling offsets and weights—realizing for the first time the adaptive dynamic evolution of convolution kernel morphology with variations in rice scale. Furthermore, ADCB is embedded into the interaction nodes of the YOLO backbone and neck; combined with depthwise separable convolution in the neck, it synergistically enhances multi-scale feature extraction from rice images. Experiments on public datasets show that ADC-YOLO comprehensively outperforms state-of-the-art algorithms in terms of AP50 and AP75 metrics and maintains stable high performance in scenarios such as small targets at the seedling stage and leaf overlap. This work provides robust technical support for intelligent rice field monitoring and advances the practical application of computer vision in precision agriculture.

Single-frame remote sensing image super-resolution can improve the spatial resolution of imaging without requiring a change in hardware, making it an important research direction in remote sensing. In recent years, owing to their excellent generative capability and training stability, diffusion models have shown great potential in the field of super-resolution remote sensing imaging. Given the rapid development of research in this field, it is necessary to conduct a comprehensive review of existing diffusion model-based super-resolution algorithms for remote sensing to help researchers accurately grasp the technical context and development trends. This paper surveys the application of diffusion models to single-frame SR remote sensing imagery. First, we elaborate on the intrinsic characteristics of remote sensing images from three perspectives—the imaging physics process, semantic features, and data scale and task objectives—thereby laying a tailored foundation for algorithm design. Subsequently, we categorize existing algorithms based on three core technical approaches: model architecture optimization, training paradigm innovation, and prior-knowledge fusion. For a comprehensive and objective evaluation of current algorithms, we compile mainstream datasets and multi-level evaluation metrics applicable to super-resolution remote sensing image tasks. Finally, combining the current technical bottlenecks, we propose four future development directions, aiming to provide theoretical support and technical guidance for the development of diffusion models in the field of super-resolution remote sensing imaging.

Infrared small-target segmentation (IRSTS) is crucial for a wide range of applications, including maritime search-and-rescue operations and intelligent traffic surveillance. However, current deep learning methods struggle with dynamic scale variations in infrared small targets, resulting in false detections and missed detections, alongside inadequate core localization accuracy. To address these challenges, we propose an infrared small-target segmentation framework founded on morphological attention and an energy core loss function, IRSTS_Unet. Specifically, we design a Dynamic Shape-adaptive Deformable Attention Module (DSDAM), which achieves parameterized feature extraction via “initial localization–offset deformation–precise sampling”. This approach enables the network to differentially focus on target cores and background cues to suppress clutter. To improve the efficiency of multi-scale feature aggregation, we embed the DSDAM within both the feature extraction and cross-layer fusion stages. Furthermore, we formulate a Core Energy-aware Core-Priority loss (CECP-Loss) function that incorporates the energy prior distribution of small targets, effectively counteracting the “core dilution” phenomenon endemic to conventional loss functions. Through extensive experiments on multiple public datasets, we demonstrate that IRSTS_U-Net outperforms state-of-the-art approaches in terms of both detection accuracy and robustness.