Autonomous aerial refueling technology can significantly extend the operational endurance of unmanned aerial vehicles (UAVs), enhancing their ability to perform long-duration missions efficiently. In this paper, we address the identification of refueling drogues in the close docking phase of autonomous aerial refueling. We propose a high-precision real-time drogue recognition network called DREP-Net. The backbone of this network employs the DGST module for efficient feature extraction and improved representation of multi-scale information. For occlusion and complex background problems, we designed the RGConv module, which combines the re-parameterization module with the GhostNet idea to improve the detection of an occluded drogue. Meanwhile, we introduced the efficient local attention mechanism into the neck network to enhance the overall attention to the target region. Then, we designed Phead, a lightweight detection head that combines the advantages of decoupling and coupling heads to improve the detection speed. Finally, we compared our network with mainstream algorithms on a real drogue dataset, and the results show that DREP-Net has 2.7% higher mean average precision (mAP) compared to the YOLOv8n model, and the detection speed is improved by 31.4 frames per second.

The accuracy and rapidity of total iron content (TFE) analysis can accelerate iron ore production. Although the conventional TFE detection methods are accurate, its detection speed presents difficulties in meeting production requirements. Therefore, this paper proposes a method of TFE detection based on reflectance spectroscopy (wavelength range: 340–2500 nm) and remote sensing. Firstly, spectral experiments were conducted on iron ore using the HR SVC-1024 spectrometer to obtain spectral data for each sample. Then, the spectra were smoothed and dimensionally reduced by using wavelet transform and principal component analysis. To improve the detection accuracy of TFE, a two hidden layer extreme learning machine with variable neuron nodes based on an improved sparrow search algorithm and batch normalization optimization (MSSA-BNVTELM) is proposed. According to the experimental results, MSSA-BNVTELM exhibited superior detection accuracy in comparison to other algorithms. In addition, this research established a remote sensing detection model using Sentinel-2 data and MSSA-BNVTEM to detect the distribution of TFE in the mining area. The distribution of TFE in the mine area was plotted based on the detection results. The results show that the remote sensing of the mine area can be useful for detection of the TFE distribution, providing assistance for the mining plan.