公开(公告)号：US20220196459A1

公开(公告)日：2022-06-23

申请号：US17491216

申请日：2021-09-30

申请人： Dibi (Chongqing) Intelligent Technology Research Institute Co., Ltd. ,  Star Institute of Intelligent Systems

发明人： Yongduan Song ,  Yujuan Wang ,  Gonglin Lu ,  Shilei Tan ,  Yating Yang ,  Chunxu Ren ,  Mingyang Liu

IPC分类号： G01G19/02 ,  G06N3/04 ,  G06N3/08

摘要： The present disclosure provides a real-time vehicle overload detection method based on a convolutional neural network (CNN). The present disclosure detects a road driving vehicle in real time with a CNN method and a you only look once (YOLO)-V3 detection algorithm, detects the number of wheels to obtain the number of axles, detects a relative wheelbase, compares the number of axles and the relative wheelbase with a national vehicle load standard to obtain a maximum load of the vehicle, and compares the maximum load with an actual load measured by a piezoelectric sensor under the vehicle, thereby implementing real-time vehicle overload detection. The present disclosure has desirable real-time detection, can implement no-parking vehicle overload detection on the road, and avoids potential traffic congestions and road traffic accidents.

公开(公告)号：US12098945B2

公开(公告)日：2024-09-24

申请号：US17491216

申请日：2021-09-30

申请人： Dibi (Chongqing) Intelligent Technology Research Institute Co., Ltd. ,  Star Institute of Intelligent Systems

发明人： Yongduan Song ,  Yujuan Wang ,  Gonglin Lu ,  Shilei Tan ,  Yating Yang ,  Chunxu Ren ,  Mingyang Liu

IPC分类号： G01G19/02 ,  G06N3/04 ,  G06N3/082

CPC分类号： G01G19/024 ,  G06N3/04 ,  G06N3/082

摘要： The present disclosure provides a real-time vehicle overload detection method based on a convolutional neural network (CNN). The present disclosure detects a road driving vehicle in real time with a CNN method and a you only look once (YOLO)-V3 detection algorithm, detects the number of wheels to obtain the number of axles, detects a relative wheelbase, compares the number of axles and the relative wheelbase with a national vehicle load standard to obtain a maximum load of the vehicle, and compares the maximum load with an actual load measured by a piezoelectric sensor under the vehicle, thereby implementing real-time vehicle overload detection. The present disclosure has desirable real-time detection, can implement no-parking vehicle overload detection on the road, and avoids potential traffic congestions and road traffic accidents.