Research On Road Obstacle Detection Technology Based On Machine Vision

The obstacles in the highway are usually one of the causes of traffic accidents.Due to the complex road environment,obstacles on the road are diverse and of different sizes,ranging from small gravel to large tires.As a result,it becomes difficult to effectively identify road areas and accurately detect obstacles on the road,especially the detection accuracy of long-distance obstacles on expressways is low.With the development of image processing technology,using computer vision to detect obstacles in the road is an important research direction at present.Therefore,this paper studies the existing problems as follows:(1)Based on semantic segmentation algorithm,the RCANet network model is designed to detect road areas and targets(pedestrians,vehicles,etc.)in the road.The problems of the existing semantic segmentation model structure in terms of detection accuracy and running time in road areas are analyzed,and the main network Efficient Net with better detection accuracy and real-time performance is adopted.According to the deployment position of vehicle-mounted and highway cameras,a regional attention mechanism module is proposed to emphasize the relative position relationship between different regional object categories in the image by using the location code associated with the location information.Meanwhile,combining the semantic information of different feature layers in the network model,the detection accuracy of the algorithm on road area and target is improved.(2)Based on the classification ideas of energy theory,generative expression and discriminant,an uncertainty evaluation algorithm is designed to estimate the probability of each pixel in the image and output the score map for road obstacle detection.In order to keep the consistency of the distribution of the known class objects and avoid the mutual influence between the models,this paper designs the Teacher-Student detection network by decoupling method,and gives the reasoning and training flow of the uncertainty evaluation algorithm on the Student network.In addition,it is proposed to use the perception difference map generated by the road area detection network to compensate the score map,so as to further improve the distinction between obstacles and non-obstacles.Finally,through the probability and statistical distribution of obstacle and non-obstacle pixels,it is verified that the algorithm designed in this paper can effectively separate the two,and improve the accuracy of detecting remote obstacles.(3)In order to apply the designed algorithm in practice,this paper uses Spring Boot,My SQL and other development frameworks to integrate the algorithm and application together,and designs a visual obstacle monitoring software.After the image or video data is read for preprocessing and obstacle detection,the results are displayed and the obstacle position is output.In addition,the detection results can be saved and queried.