| dc.description.abstract | In the thesis, deep learning technology is used for outdoor obstacle identification and obstacle distance detection. Meanwhile, a set of outdoor wearable guide device is designed to provide a safer and more portable guide system for visually impaired people during outdoor walking.
Obstacle distance detection points in two ways, one is for the use of monocular camera through monocular depth estimation neural network to get the disparity image of the original image, through the regression analysis to the disparity image is converted to a depth image, image on obstacle detection area through the histogram statistics way to calculate obstacle distance output value, then calculate the obstacle height and position according to the obstacle distance. Obstacle detection uses semantic segmentation neural network or object detection neural network, calculate the obstacle distance from the obstacle identification results. Among them, after modification of the neural network of object detection, the rotation angle of the bounding box can be predicted to make the bounding box fit the obstacle better. Combined with the above obstacle distance detection results, the semantic segmentation collocation monocular depth is applied to the blind guide robot, and the object detection collocation monocular depth is applied to the wearable guide device for subsequent obstacle avoidance control. Second for the use of the stereo camera to calculate the depth image of the original image, using the object detection to identify obstacles, image on obstacle detection area in depth of each pixel values arranged from low to high, take the first quartile as obstacle distance, depth from stereo camera collocation object detection is also used in wearable guide device for the obstacle avoidance control, but high speed of operation, at the same time can be integrated signboard tracking system.
Based on security considerations, visually impaired people need to walk on the right side of the road. The thesis designs a keep to right side algorithms, according to the camera position height and the camera intrinsic, use the perspective projection method to find out the actual depth and width of the projection to the coordinate of image plane. According to this method, drawing the reference line of the width distance from road to user, as well as the user′s left half body width of the reference line, formed the reference line of both sides. Match the semantic segmentation to locate the road area , in accordance with the relative relationship between width on both sides of the reference line and road edge, reminding the visually impaired that do corrections by going straight, moving to the left or right, and the rotation. At the same time, based on the obstacle messages, setting of obstacle avoidance control method is designed to perform actions such as avoiding obstacles, stepping over obstacles or stopping walking, according to the height, orientation and distance from the obstacles. Combined with each algorithm, leading the visually impaired to the destination. | en_US |