Abstract
Although the current vehicle detection and recognition framework based on deep learning has its own characteristics and advantages, it is difficult to effectively combine multi-scale and multi category vehicle features, and there is still room for improvement in vehicle detection and recognition performance. Based on this, an improved fast R-CNN convolutional neural network is proposed to detect dim targets in complex traffic environment. The deep learning model of fast R-CNN convolutional neural network is introduced into the image recognition of complex traffic environment, and a structure optimization method is proposed, which replaces VGG16 in fast RCNN with RESNET to make it suitable for small target recognition in complex background. Max pooling is the down sampling method, and then feature pyramid network is introduced into RPN to generate target candidate box to optimize the structure of convolutional neural network. After training with 1497 images, the complex traffic environment images are identified and tested. The results show that the accuracy of the proposed method is better than other comparison methods, and the highest accuracy is 94.7%.
Highlights
IntroductionImages are an important source of information for human beings, and vision is one of the main ways to receive information from the outside world in daily life
The deep learning model of fast R-CNN convolutional neural network is introduced into the image recognition of complex traffic environment, and a structure optimization method is proposed, which replaces vgg16 in fast RCNN with RESNET to make it suitable for small target recognition in complex background
Represents the number of correctly identified vehicles and dim targets; nFP represents the number of misidentified vehicles and dim targets; nFN represents the number of unidentified vehicles and dim targets
Summary
Images are an important source of information for human beings, and vision is one of the main ways to receive information from the outside world in daily life. With the upgrading of hardware equipment, the rapid progress of deep learning technology and the development of big data, the universal combination of target detection and recognition technology and the industry has made its application scope wider and wider. The precise detection and recognition of various targets by target detection and recognition technology laid the foundation for the development of video surveillance, unmanned driving, scene semantic understanding, Internet mobile terminals, image retrieval and other fields [3-5]
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