Research On Low Illumination Image Enhancement And Vehicle Recognition Technology

With the widespread popularity of automobiles and the drawbacks of traditional vehicle driving methods,unmanned driving technology and its assistive technologies have become an important development direction for automobiles in the future.Unmanned driving technology as a new type of driving technology,has a far-reaching impact in problems such as widening the driving field,reducing traffic accidents and solving traffic jam.This paper takes unmanned driving as the starting point to study the application of machine vision in vehicle detection in low-illumination environments.The application of unmanned driving technology needs to satisfy the requirements of normal vehicles driving under a variety of undesirable factors.Low light environment is one of the main undesirable factors,and vehicles are the most important detection targets in unmanned driving.In order to expand the application field of driverless driving,this paper improves the low illumination image enhancement algorithm and implements the detection of vehicles on this basis.The main research contents of this article are as follows:1.System overall scheme design.On the basis of studying the development status at home and abroad,the overall scheme of low-illuminance image enhancement and vehicle recognition is designed,including the arrangement process of the scheme,related software and the collection of low-illuminance image data sets.2.An improved low-illumination image enhancement algorithm is proposed.The low-light image enhancement algorithm can improve the useful information in the low-light image,thereby improving the image quality.Based on the research and analysis of the traditional low-illumination image enhancement algorithm,this paper evaluates the traditional low-illumination image enhancement algorithm through the algorithm principle and low-illumination image enhancement experiment results,and combines the requirements of the low-illumination image enhancement algorithm in the field.Select and improve traditional low-light image enhancement algorithms.3.Analyze the performance of improved low-light image enhancement algorithms.In order to evaluate the advantages and disadvantages of the improved lowillumination image enhancement algorithm compared to other enhancement algorithms in low-illuminance image enhancement,the performance of each enhancement algorithm is analyzed through comparative experiments.Based on the analysis of the evaluation index of the algorithm,multiple indexes such as absolute subjective evaluation index,standard deviation,information entropy,grayscale variance product,edge strength and algorithm operation time are selected to objectively measure the experimental results,and the advantages of the improved low-illumination image enhancement algorithm in the research field of this paper are verified.4.Experiment and selection of denoising algorithm.The low-illuminance image contains a lot of noise,and the image needs to be reasonably denoised to meet the requirements of subsequent vehicle identification.The characteristics of each denoising algorithm are analyzed through the principle of denoising algorithm and experiments,and combined with the requirement to retain the characteristics of vehicle recognition,bilateral filtering is finally selected as the denoising algorithm.5.Recognition of vehicles and filtering of false detection areas.In order to verify the practicability of the improved algorithm,a vehicle detection experiment needs to be performed on the enhanced low-illuminance image.Through research and comparison of vehicle detection methods,the gradient histogram is selected as the feature of vehicle recognition,and the support vector machine is selected as the classifier.Finally,the region of interest and four sliding windows of different sizes are selected.The vehicle is tested.Misdetection is easy to occur in low-illuminance image recognition.Therefore,the area of the misdetection is filtered by heat map,and finally recognition of the vehicle is achieved,which verifies the practicability of the improved algorithm.