公开(公告)号：US20250005914A1

公开(公告)日：2025-01-02

申请号：US18711367

申请日：2023-04-21

Applicant: NANJING UNIVERSITY OF SCIENCE AND TECHNOLOGY

Inventor： Yong QI ,  Xin ZENG

IPC: G06V10/80 ,  G06V10/77 ,  G06V10/82 ,  G06V20/54 ,  G06V20/56

Abstract: The present disclosure provides a multi-task panoptic driving perception method and system based on improved You Only Look Once version 5 (YOLOv5). The method in the present disclosure includes: performing image preprocessing on an image in a dataset to obtain an input image; extracting a feature of the input image by using a backbone network of improved YOLOv5, to obtain a feature map, where the backbone network is obtained by replacing a C3 module in a backbone network of YOLOv5 with an inverted residual bottleneck module; inputting the feature map into a neck network to obtain a feature map, and fusing the obtained feature map and the feature map obtained by the backbone network; inputting the fused feature map into a detection head to perform traffic target detection; and inputting the feature map of the neck network into a branch network to perform lane line detection and drivable area segmentation.

公开(公告)号：US20250078516A1

公开(公告)日：2025-03-06

申请号：US18681766

申请日：2023-04-20

Applicant: NANJING UNIVERSITY OF SCIENCE AND TECHNOLOGY

Inventor： Yong QI ,  Xin ZENG ,  Yuan ZHUANG

IPC: G06V20/54 ,  G06V10/75 ,  G06V10/80 ,  G06V20/70 ,  G08G1/017

Abstract: The present disclosure discloses a method and a system for detecting an abnormal traffic behavior. The method of the present disclosure includes: retaining an abnormal static target vehicle in a traffic surveillance video in a background through background modeling; performing abnormal target detection, and obtaining a cropped picture of an abnormal target vehicle and a cropped video clip through cropping; performing anomaly start time estimation, inputting the cropped picture and the cropped video clip to a network model combining twin cross-correlation with pseudo three-dimensional (P3D)-Attention, labeling a classification label on the cropped video clip, and determining a video frame when abnormal behavior occurs; and determining whether a to-be-matched vehicle is an abnormal target vehicle, and determining a start time and an end time of abnormal traffic behavior with reference to the video frame that is obtained when the abnormal behavior occurs.