Vehicle detection in remote sensing images faces significant challenges due to small object sizes, scale variation, and cluttered backgrounds. To address these issues, we propose RSW-YOLO, an enhanced detection model built upon the YOLOv8n framework, designed to improve feature extraction and robustness against environmental noise. A Restormer module is incorporated into the backbone to model long-range dependencies via self-attention, enabling better handling of multi-scale features and complex scenes. A dedicated detection head is introduced for small objects, focusing on critical channels while suppressing irrelevant information. Additionally, the original CIoU loss is replaced with WIoU, which dynamically reweights predicted boxes based on their quality, enhancing localization accuracy and stability. Experimental results on the DJCAR dataset show mAP@0.5 and mAP@0.5:0.95 improvements of 5.4% and 6.2%, respectively, and corresponding gains of 4.3% and 2.6% on the VisDrone dataset. These results demonstrate that RSW-YOLO offers a robust and accurate solution for UAV-based vehicle detection, particularly in urban scenes with dense or small targets.

This paper proposes an optimized algorithm based on YOLOv11s to address the problem of insufficient detection accuracy of vehicle targets from a drone perspective due to certain scenes involving complex backgrounds, dense vehicle targets, and/or large variations in vehicle target scales due to oblique imaging. The proposed algorithm enhances the model’s local feature extraction capability through a module collaboration optimization strategy, integrates coordinate convolution to strengthen spatial perception, and introduces a small object detection head to address target size variations caused by altitude changes. Additionally, we construct a dedicated dataset for urban vehicle detection that is characterized by high-resolution images, a large sample size, and low training resource requirements. Experimental results show that the proposed algorithm achieves gains of 1.9% in precision, 6.0% in recall, 4.2% in mAP@0.5, and 3.3% in mAP@0.5:0.95 compared to the baseline network. The improved model also achieves the highest F1-score, indicating an optimal balance between precision and recall.

Remote sensing images used in military reconnaissance contain a large amount of sensitive information, and any leakage may pose a serious threat to national security. To address the need for high-precision detection of sensitive targets and to mitigate information leakage risks, this study proposes a remote sensing image processing framework that integrates multi-object detection with hierarchical chaotic encryption. Based on the YOLOv7-tiny architecture, a Multi-branch Enhanced Feature Aggregation Block (MEFABlock) is designed, which incorporates multi-scale convolutions and attention mechanisms to effectively enhance feature extraction in complex remote sensing scenes. In addition, a coordinate convolution module is introduced before the detection head to strengthen spatial position modeling, thereby achieving higher detection accuracy while maintaining a lightweight network structure.In the encryption stage, a novel two-dimensional chaotic system is constructed, and an improved hash-based method is proposed to generate the initial parameters of the chaotic system. The Josephus-ring permutation process is further enhanced, and a three-stage diffusion–permutation–diffusion encryption structure is employed. The improved detection network is first used to precisely locate sensitive regions in the image; the regions inside the detected bounding boxes are then locally encrypted and embedded back into the original image to achieve information concealment. Finally, global encryption is performed using the chaotic system to ensure end-to-end data security.Experiments conducted on the publicly available MAR20 military aircraft remote sensing dataset demonstrate that the proposed method improves Precision (P) by 2.4%, Recall (R) by 2.7%, mAP@0.5 by 2.7%, and mAP@0.5:0.95 by 2.2% compared with the baseline model. The encrypted remote sensing images achieve an information entropy of 7.9997, and meet high security standards in key metrics such as NPCR and UACI. Overall, the proposed encryption framework achieves high target detection accuracy and strong information protection performance, exhibiting robust potential for practical engineering applications.

Fourier ptychographic microscopy, as a computational imaging method, can reconstruct high-resolution images but suffers optical aberration, which affects its imaging quality. For this reason, this paper proposes a network model for simulating the forward imaging process in the Tensorflow framework using samples and coherent transfer functions as the input. The proposed model improves the introduced Wirtinger flow algorithm, retains the central idea, simplifies the calculation process, and optimizes the update through back propagation. In addition, Zernike polynomials are used to accurately estimate aberration. The simulation and experimental results show that this method can effectively improve the accuracy of aberration correction, maintain good correction performance under complex scenes, and reduce the influence of optical aberration on imaging quality.