In recent years, object detection using aerial images captured by unmanned aerial vehicles (UAVs) has become a research hotspot. However, due to the high resolution of UAV imagery, large variations in object scales, and the predominance of small targets, achieving fast and accurate object classification remains a significant challenge. To address these issues, this paper proposes a lightweight feature enhancement attention network, termed LFEA-YOLO. First, a multi-scale feature fusion (MSFF) module is introduced, which establishes a cross-scale feature interaction mechanism to effectively integrate semantic information with spatial details while reducing the computational overhead caused by redundant features. Second, deformable convolution networks (DCNs) are incorporated into the neck network to construct the DCNv4-C2F module. Leveraging its adaptive spatial sampling capability, this module dynamically adjusts the receptive field of convolution kernels, thereby overcoming the limitations of traditional fixed-grid sampling and enhancing the network’s ability to model geometric variations of small-scale objects. Finally, an attention-enhanced detection head (AE-Head) is designed, integrating a large separable kernel attention (LSKA) mechanism to dynamically emphasize discriminative features in key target regions. Experimental results on the VisDrone2019 and DOTAv1.0 datasets demonstrate significant improvements over the baseline model, with ${\mathrm{mAP}}_{50}$ and ${\mathrm{mAP}}_{50:95}$ increasing by 10.6% and 7.8%, respectively. Compared with several recently proposed models, LFEA-YOLO also exhibits clear advantages, validating the effectiveness of the proposed approach.

Deep joint source-channel coding (DeepJSCC) has emerged as a promising paradigm for semantic-aware wireless image transmission, achieving strong performance under challenging channel conditions. However, MSE-trained DeepJSCC systems typically achieve high peak signal-to-noise ratio (PSNR) values but suppress high-frequency details, resulting in perceptually blurry reconstructions that fail to capture fine textures and edge information. Existing perceptual enhancement approaches for JSCC systems face significant practical limitations: full transceiver redesign methods require replacing both the transmitter and the receiver with large models (19–31 million parameters), incurring substantial deployment costs; diffusion-based refinement approaches require over 1700 million additional parameters and introduce inference latency exceeding 13 s, rendering them unsuitable for latency-constrained wireless applications; and generic image restoration networks lack channel state awareness and cannot adapt to varying signal-to-noise ratio (SNR) conditions. This paper proposes a lightweight receiver-only perceptual enhancer designed for use with frozen DeepJSCC backbones. The proposed module adopts residual learning with feature-wise linear modulation (FiLM)-based SNR-adaptive modulation to dynamically adjust the enhancement strength under varying channel conditions. A radially weighted FFT magnitude loss is further introduced to guide high-frequency recovery. The enhancer adds only 0.29 million trainable parameters (<1% of the backbone) and requires neither transmitter modification nor backbone retraining. Extensive experiments on the Kodak24 and DIV2K datasets demonstrate a 34.4–37.5% LPIPS reduction over the frozen DeepJSCC baseline under AWGN channels. Supplementary robustness evaluations further show a 30–33% LPIPS reduction under Rayleigh fading, and stable generalization to unseen SNR levels. The receiver-side decoder-plus-enhancer pipeline requires 43 ms at 768 × 512 resolution, corresponding to approximately 23 frames per second.

In aerial image small-object detection, complex imaging perspectives, arbitrary object orientations, and long-tailed category distributions jointly exacerbate sample imbalance, which significantly degrades detection stability and leads to frequent misclassification of minority categories. To address these challenges, this paper proposes a novel training framework termed SCUD. Specifically, in the label noise suppression strategy (LNSS), a contrastive learning mechanism based on semantic consistency is introduced to constrain the aggregation of similar samples in the feature space, thereby reducing the adverse impact of noisy samples on model optimization. In addition, a scale-aware resampling strategy (SARS) is designed to alleviate noise amplification and overfitting caused by excessive repetition of small objects during training. Furthermore, an adaptive instance selection mechanism (AISM) is developed by jointly modeling prediction uncertainty and global statistical priors, enabling the model to dynamically emphasize learning from informative samples. Extensive experiments are conducted on two publicly available unmanned aerial vehicle (UAV) aerial image datasets to validate the effectiveness of the proposed approach. The proposed method achieves an mAP50 of 70.7% on the DOTA-v1.0 dataset and 88.1% on the DIOR dataset. Notably, the detection accuracy of several rare categories is significantly improved, further demonstrating the effectiveness of the proposed method in addressing sample imbalance in aerial image small-object detection.