The invention discloses an auxiliary obstacle
perception method for
visually impaired people based on an improved YOLO model, and the method comprises the steps: employing
Darknet-YOLOv3 as a framework, and employing
Darknet-53 as a
feature extraction backbone network; according to the YOLOV3
algorithm, carrying out
feature fusion by using a feature map up-sampling idea in a feature
pyramid network FPN, so that the precision of
small target detection is improved, and various common obstacles, including road cones, stone balls, isolation columns, forbidden cross bars, handrails, fire hydrants, plants, people, pits, water pits and the like, on sidewalks can be detected and identified. The method can identify various identifications and targets at traffic intersections, including zebra crossings,
signal lamps, bicycles, motorcycles, vehicles, people and the like, and can also judge upstairs, downstairs, various steps and some other obstacle targets of unknown types.