Chenghao Ning

Remote-sensing object detection commonly requires repeated training on high-resolution aerial and satellite imagery, where targets may be small, densely distributed, and surrounded by extensive background. For remote-sensing detection tasks that require shorter model-training cycles, reducing training time without sacrificing detection accuracy is important. The Anti-Forgetting Sampling Strategy (AFSS) reduces training time by avoiding repeated processing of learned images, but its fixed warm-up may start sampling before the detector is mature and thereby reduce accuracy. We propose Maturity-Gated AFSS (MG-AFSS), a detector-maturity activation controller for AFSS. The method accumulates validation mean average precision at an intersection-over-union threshold of 0.50 (mAP50), fits a cumulative saturating curve online, and enables AFSS only when the estimated maturity indicates trustworthy image states. After activation, it reuses the original AFSS sampling rule. We evaluate MG-AFSS with matched lightweight detectors on five public remote-sensing datasets: NWPU VHR for the main three-seed study and additional settings; UCAS-AOD for horizontal-box detection; HRSC2016 and ShipRSImageNet for oriented bounding-box (OBB) boundary cases; and DOTAv1 for a larger-scale OBB block. In the three-seed, 80-epoch, from-scratch NWPU VHR experiment, MG-AFSS improves over AFSS by +0.0264 mAP50 and +0.0319 mAP50–95, where mAP50–95 denotes mean average precision averaged over intersection-over-union thresholds from 0.50 to 0.95. It keeps mAP50–95 essentially equal to standard full-dataset training (+0.0003), with a training-time ratio of 0.867 relative to standard training. In a pretrained NWPU setting, the training-time ratio is 0.557 relative to matched standard training, with a higher checkpoint mAP50. In the DOTAv1 block, MG-AFSS has a training-time ratio of 0.759, lower than AFSS at 0.777; its checkpoint-best differences from standard training are −0.0095 mAP50 and −0.0149 mAP50–95, both smaller in magnitude than those of AFSS. Overall, MG-AFSS yields a conditional accuracy–training-time trade-off: it reduces training time when the maturity condition is satisfied and, as observed in the two smaller OBB boundary runs, retains full-dataset training otherwise.

Infrared small target detection (IRSTD) is the pivotal technology for remote surveillance and search missions under infrared imagery. Due to the target’s nature of being small, dim, and lacking texture information, detection algorithms for relatively large visible targets are not suitable for infrared small targets. In this letter, a real-time, robust IRSTD network with feature enhancement and augmentation, FEA-Net, is proposed, which includes a hybrid multiscale enhancement (HME) module and a saliency detection network with a small target feature augmentation (STFA) module. The HME introduces a hybrid multiscale spatial attention mechanism to obtain a rough estimation of targets and then apply it to the original image. The STFA is directed against the insufficiency of target-related features and the consequent feature losses, which screens out the crucial information and represents it into a larger tensor for further feature fusion, increasing the proportion of target-related features of the output. The results show that the proposed method can achieve the same level of detection effect as the comparable methods and still has considerable real-time performance with the normalized intersection over union (nIoU) of 76.12% on the SIRST dataset and 92.28% on the NUDT-SIRST dataset.

Linear servomotors (LSM) are widely used in industries. A LSM requires a mover positioning tool for precise control. A Vision-based digital image grating (DIG) measurement for LSM mover positioning is proposed in this study with advantages of simple system, low cost, and good robustness to environments. It estimates the LSM's rough position according to four mark-lines and obtains accurate LSM's positions by a fast least-square (FLS) image-matching algorithm. As a result, it can measure the LSM's position accurately in a wide range. The FLS proposed here represents the least-square cost function by the transformation parameters explicitly, and then calculates the parameters directly. Therefore, the image resampling process, which is inevitable for the existing least-square methods is not necessary. Simulations prove that the FLS is approximately 4.4 times faster than the classic inverse compositional Gauss–Newton (IC-GN) algorithm, and that the FLS is more accurate and robust than the IC-GN. Experiments show that the proposed DIG method can measure the LSM mover positions with an accuracy of 0.75 μ m mean absolute error.

Space exploration missions involve significant participation from astronauts. Therefore, it is of great practical importance to assess the astronauts’ performance via various parameters in the cramped and weightless space station. In this paper, we proposed a calibration-free multi-view vision system for astronaut performance capture, including two modules: (1) an alternating iterative optimization of the camera pose and human pose is implemented to calibrate the extrinsic camera parameters with detected 2D keypoints. (2) Scale factors are restricted by the limb length to recover the real-world scale and the shape parameters are refined for subsequent postural reconstruction. These two modules can provide effective and efficient motion capture in a weightless space station. Extensive experiments using public datasets and the ground verification test data demonstrated the accuracy of the estimated camera pose and the effectiveness of the reconstructed human pose.