Remote sensing image object detection and instance segmentation are widely valued research fields. A convolutional neural network (CNN) has shown defects in the object detection of remote sensing images. In recent years, the number of studies on transformer-based models increased, and these studies achieved good results. However, transformers still suffer from poor small object detection and unsatisfactory edge detail segmentation. In order to solve these problems, we improved the Swin transformer based on the advantages of transformers and CNNs, and designed a local perception Swin transformer (LPSW) backbone to enhance the local perception of the network and to improve the detection accuracy of small-scale objects. We also designed a spatial attention interleaved execution cascade (SAIEC) network framework, which helped to strengthen the segmentation accuracy of the network. Due to the lack of remote sensing mask datasets, the MRS-1800 remote sensing mask dataset was created. Finally, we combined the proposed backbone with the new network framework and conducted experiments on this MRS-1800 dataset. Compared with the Swin transformer, the proposed model improved the mask AP by 1.7%, mask APS by 3.6%, AP by 1.1% and APS by 4.6%, demonstrating its effectiveness and feasibility.
The gradual development of remote sensing object tracking technology based on unmanned aerial vehicles (UAV) videos has become one of the main research directions in the field of visual tracking. However, due to characteristics of the UAV platform, typical visual tracking algorithms currently applied to natural scenes cannot be used directly. Small-scale objects in UAV remote sensing videos are difficult to detect and have the problem of tracking identity switching. In order to solve these problems, we designed the Swin transformer neck YOLOX (STN-YOLOX) object detection algorithm as the detection module, and the G-Byte data association method as the tracking module. We then combined the two into a new multi-object tracking algorithm named STN-Track. We used STN-Track to conduct experiments on the UAVDT and VisDrone MOT datasets. The experimental results show that compared with the current state-of-the-art (SOTA) methods, our STN-Track has improved detection and tracking accuracy of small-scale objects and greatly improved identification capabilities for object tracking. Compared with the SOTA ByteTrack algorithm, MOTA of STN-Track can be improved by up to 3.2%, APS can be improved by up to 4.4%, MT can be improved by up to 6.8%, and IDSW can be reduced by up to 28.0%.
Background: Correct species identification is the most crucial step in applying entomological evidence to estimate the postmortem interval (PMI) since death of decomposed corpses. Wing morphometrics have been proposed in species classification as an alternative method of traditional morphology and molecular approaches. However, so far, this method has not been applied to the identification of Chinese Calliphoridae and few studies compare the two identification methods.Methods: We used landmark-based geometric morphometrics of wings to identify nine medically and forensically important blow fly species of China. 270 specimens representing nine species and eight genera were sampled, 18 landmarks on the right wing were measured and analyzed using canonical variates analysis and discriminant function analysis. Then, a cross-validation test was used to evaluate reliability of the method. Moreover, in order to further assess the validity of this method, molecular identification is used for comparative analysis. Eighty sequences of cytochrome c oxidase subunit I (COI) of Calliphoridae isolated from different countries were downloaded from Genbank, including the data previously submitted by our team. Results: Different species and genera can be well separated through morphometric analysis with an overall classification accuracy of 80~100%, but discrimination between sexes was less effective. The results indicated that the discriminative efficiency of the two methods is almost identical.Conclusions: Wing morphometrics can be used as a complementary method of molecular identification for the geographical location and gender identification of certain species as a simple and cheap method.
The decoherence effect of a laser caused by a speckle field seriously restricts the development of heterodyne lidar. To address this problem, we proposed a spatial decoherence compensation algorithm, whose feasibility was proved by experiments with a system featuring simple structure and convenient operation. The results demonstrated that the speed of the proposed algorithm was several orders higher than that of other algorithms and the system SNR was increased by a maximum of 1464 times after the algorithm processing. The proposed algorithm can process the signal in real time and effectively, having great application potential in long-distance weak target detection.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.