A framework for visual small target detection and tracking is introduced, leveraging Unmanned Aerial Vehicle (UAV) Remote Sensing Images (RSIs). The proposed Cropped Target Detection and Tracking (CTDT) framework comprises two integral stages: the detection stage and the tracking stage. During the detection stage, all targets can be identified from RSIs, providing a basis for the subsequent single-object tracking stage. Both stages are based on a cropping and random sampling strategy: the RSI is cropped into Small-Sized Images (SSIs), from which a random batch is constantly selected without repetition and fed into a network to locate the target until the target is discovered or all SSIs are used. This strategy improves the efficiency of detection and tracking. After cropping, the target may appear in multiple SSIs, and the target in each SSI may be incomplete. A Cropped Target Feature Extraction (CTFE) network is designed to detect and track the target by leveraging the information from small and incomplete targets in SSIs. CTFE achieves high precision and meets real-time requirements. The performance analysis of the detection network is also conducted in detail, and the results are instrumental in informing the design of the tracking network. By utilizing three UAV RSI datasets (UAVDT, UAV123, and DTB70), CTDT is compared to numerous state-of-the-art mainstream methods, such as PVT++, SiamBAN, SmallTrack, SiamAPN++, SiamIRCA, SiamFC, and CSK, to confirm its superiority and real-time performance. The results affirm that the proposed framework exhibits outstanding performance and adaptability to fast-moving targets, target loss, and camera failures, and holds promise for real-time applications. Additionally, real-world tests on a typical UAV platform demonstrate excellent performance and efficiency in a variety of UAV-specific tasks, as well as transferability for new missions.

Due to the characteristics of high Mach number, long flight time and large overload, the new generation of hypersonic vehicle is facing extreme aerodynamic heating and complex mechanical environment, which poses a serious challenge to drag and heat reduction performance. This paper focuses on the research methods of active mass ejection drag and heat reduction in the boundary layer of hypersonic vehicles, and systematically reviews the research progress and paradigm transformation path in this field. Firstly, the development status of traditional research paradigm—experiment, theory and numerical method is comprehensively reviewed, and the core challenges are revealed: the bottleneck of experimental data sparsity and multi-field coupling measurement, the ‘precision-efficiency’ trade-off dilemma of numerical simulation, and the lack of multi-scale coupling modeling theory and method. Then, based on these challenges, a new paradigm of intelligent scientific research with data-driven, physical information fusion and multi-scale coupling as the core is proposed, and its technical classification and frontier progress are summarized. Finally, through the application cases in the fluid mechanics scenario, the innovation mechanism of the new paradigm and its advantages and potential application ideas in solving the challenges of traditional research paradigms are deeply analyzed. The purpose is to stimulate the interest of researchers through in-depth insight into the new paradigm of intelligent scientific research, promote the sustainable development and paradigm transition of drag and heat reduction research of hypersonic vehicles, and provide important reference and inspiration for the subsequent exploration of the improvement of drag and heat reduction performance of the new-generation hypersonic vehicles.

Benefiting from deep learning methods, the performance of object detection methods has greatly improved in recent years. However, significant challenges still exist in detecting targets from UAV remote sensing images. For example, the targets in UAV remote sensing images have small resolution and complex background, and the existing algorithms are difficult to meet the requirement for real-timeliness. To overcome these challenges, this paper proposes a Real-Time Small Target Detection (RTSTD) method based on a Multi-scalar & Multi-depth Feature Extraction (MMFE) network, which can efficiently detect small targets from UAV remote sensing images. The proposed RTSTD crops an input image into multiple small-size images, and feeds a portion of these small-size images into the lightweight MMFE network. Therefore, RTSTD has the capability to handle remote sensing images of arbitrary resolutions without losing image features. A more effective output is proposed for the MMFE network: an overlap vector that represents the position and confidence of the target in the input image. To enhance the MMFE network’s ability to distinguish targets from complex backgrounds, the positive and negative samples are redefined. To test the performance of RTSTD, seven datasets are selected and reconstructed from UAV123, DTB70 and AU-AIR, comprising a total of 8, 369 UAV remote sensing images involving small target detection in the ground and sea scenarios. The experimental results demonstrate that compared to existing detection methods, the RTSTD method achieves improvements in both accuracy and speed. It achieves an F-Score of 0.90 or above, with a running speed of over 66 frames per second (FPS) using GPU acceleration and over 35 FPS using only CPU.