Application of acquiring region of interest based on the YOLOv5 model in terahertz digital holography.

The imaging process of terahertz in-line digital holography is susceptible to environmental interference, and it is difficult to obtain high-quality images and image segmentation results. The challenge can be better handled by using the region of interest (ROI) condition to improve the image quality of the object region and reduce the interference of peripheral noise. In this paper, for two-dimensional 2.52 THz in-line digital holography, a method to add a variety of real backgrounds in the simulation scene is proposed, and through this method, a sufficient amount of close-to-real-scene images are produced to train the YOLOv5 model. The object ROI is extracted by the YOLOv5 model (YOLOv5-ROI). Based on the region, a new, to the best of our knowledge, support-domain-constrained phase retrieval algorithm (YOLOv5-ROI-SPRA) and an image segmentation method combined with the Otsu method (YOLOv5-ROI-Otsu) are proposed, respectively. The better results show that the expected purpose is achieved.

Logical Topology Inference via CPGCN Joint Optimizing With Pedestrian Re-Id.

With the rise of artificial intelligence, deep learning has become the main research method of pedestrian recognition re-identification (re-id). However, most of the existing researches usually just determine the retrieval order based on the geographical location of cameras, which ignore the spatio-temporal logic characteristics of pedestrian flow. Furthermore, most of these methods rely on common object detection to detect and match pedestrians directly, which will separate the logical connection between videos from different cameras. In this research, a novel pedestrian re-identification model assisted by logical topological inference is proposed, which includes: 1) a joint optimization mechanism of pedestrian re-identification and multicamera logical topology inference, which makes the multicamera logical topology provides the retrieval order and the confidence for re-identification. And meanwhile, the results of pedestrian re-identification as a feedback modify logical topological inference; 2) a dynamic spatio-temporal information driving logical topology inference method via conditional probability graph convolution network (CPGCN) with random forest-based transition activation mechanism (RF-TAM) is proposed, which focuses on the pedestrian's walking direction at different moments; and 3) a pedestrian group cluster graph convolution network (GC-GCN) is designed to measure the correlation between embedded pedestrian features. Some experimental analyses and real scene experiments on datasets CUHK-SYSU, PRW, SLP, and UJS-reID indicate that the designed model can achieve a better logical topology inference with an accuracy of 87.3% and achieve the top-1 accuracy of 77.4% and the mAP accuracy of 74.3% for pedestrian re-identification.