Objective

2

The advent of driverless cars has become a hot topic in today's society. Driverless aims to achieve a high degree of autonomous driving behavior through environmental awareness

such as in starting

braking

lane line tracking

lane changing

collision avoidance

and parking. Image segmentation of road scenes plays an important role in this technology. Studying the manner in which complex scenes and high-efficiency scene segmentation images in the environment of severe noise interference are achieved is essential. Traditional road segmentation generally uses a binocular stereo vision map and a motion indicator-based approach. For example

some researchers proposed a pedestrian detection based on binocular stereo vision and SVM(support vector machine) algorithm and used threshold segmentation to determine the coordinate position of a moving target. For the diversity of motion indicators

other researchers used the projection surface direction and object. Multiple motion indicators

such as altitude and feature tracking density

segment the road. However

these methods have high requirements on computing resources. For the current unmanned practicality requirements

a concise and resource-intensive method is required. Since 2012

deep learning has been gradually introduced into road scene segmentation. A scholar proposed a smart car steering study based on end-to-end depth learning and obtained good road feature coding through pretraining self-encoding. In recent years

AI technology has suddenly caught the interest of scholars. Computer GPU parallel operation

computational acceleration

storage space compression

and other technologies are studied and developed. Large amount of data and calculation are no longer restricted. Convolutional neural network (CNN) has become a research hotspot and has been widely used. Some researchers studied the deep learning algorithm of CNN to learn high-order features in a scene to achieve road scene segmentation. However

to some extent

although the computational strength is reduced

some problems of over-segmentation of complex scenes. Other researchers proposed the feature automatic extraction capability of deep structure using deep CNN for complex scene problems; it is a method of feature self-encoder versus feature similarity metric in source-target scenarios. However

these algorithms do not achieve the desired results for road marking

vehicle

and pedestrian segmentation accuracy. During rainy days

snowy days

and high-temperature weather

road surface often appears to be divided. With the continuous introduction of autonomous driving technology

the study of road scene segmentation algorithms in machine vision has become crucial. Most researchers in the traditional methods use machine learning to segment thresholds. The introduction of deep learning in recent years has caused the wide usage of neural network in this field.

Method

2

A road scene combining KSW(key seat wiper) and full CNN (FCNN) is proposed to address the problem of the traditional threshold segmentation method in terms of difficulty in extracting the road image threshold under multiple scenes and the training of data directly by deep neural network causing over-segmentation. The segmentation method

which combines the KSW entropy method and genetic algorithm

uses depth learning to extract features in different scenarios and applies it to the road segmentation of unmanned technology. First

the original test image of the road scene is converted into gray image

and the filtering effect is achieved by KSW genetic algorithm two-dimensional threshold segmentation; thus

the road water

road standard line

trees

and other scenes are clear in the image

and the preprocessing training set is obtained. Import pre-trained datasets into the FCNN framework After tens of thousands of training iterations

it learns and amends the weights

thereby resulting in an effective training model. Finally

the training model can be used to implement any road scene graph segmentation.

Result

2

Experimental results show that the segmentation accuracy of the sky and trees reached 91.3% and 94.3% in the KITTI dataset

respectively

and the segmentation progress of roads

vehicles

and pedestrians increased by approximately 2%. In comparison with the previous super parsing and boosting algorithms

the proposed algorithm can distinguish road segmentation lines and increase the segmentation accuracy of trees

vehicles

and pedestrians by approximately 20%. Comparing with the result of SegNet and ResNet depth networks

in the environment of roads and trees. The segmentation accuracy of road segmentation lines is relatively improved by approximately 5%. In comparison with the original image

the KSW two-dimensional threshold and genetic algorithm filter out the excessively bright part of the sun on the road and the overly bright part of the sky to prevent over-segmentation of the segmented image.

Conclusion

2

Segmentation result shows that the over-segmentation of water accumulation and mud on the road has been remarkably improved. In comparison with the traditional machine learning road scene segmentation method

the proposed method improves the segmentation accuracy to a certain extent. In comparison with the depth learning method

the proposed method is directly applied to the road scene segmentation. This method avoids the over-segmentation phenomenon to some extent and improves the model robustness. In summary

the proposed road scene segmentation algorithm combined with KSW and FCNN has broad research prospects and is expected to be applied to the processing of medical and remote sensing images.