A road environment visual perception method based on improved faster R-CNN

Abstract

A road environment visual perception method based on improved Faster R‑CNN. To meet the high-precision requirements of target detection and recognition in complex road scenes, the present invention proposes an improved Faster R-CNN algorithm based on multi-GPU training. The algorithm of the present invention uses the method of multi-GPU parallel training to improve the training efficiency; adopts the ResNet-101 feature extraction network to improve the target detection accuracy; adopts the Soft-NMS algorithm to reduce the missed detection rate; introduces OHEM into the ROI NetWork to reduce the false alarm rate; for Improve the target detection effect of the algorithm in rainy, snowy and foggy weather, and train the model with the internationally recognized autonomous driving data sets KITTI and Oxford RobotCar. Experimental results prove that the algorithm of the present invention has significantly improved training speed and detection accuracy compared with Faster R-CNN, especially in automatic driving scenarios with good generalization ability and stronger practicability.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to a road environment visual perception method based on improved Faster R-CNN. Through the improvement of the Faster R-CNN algorithm, this method has significantly improved the network model training speed and target detection accuracy, especially in automatic driving scenarios with good generalization ability and stronger practicability. Background technique [0002] The self-driving car milestone began in 2009 when Google began developing a self-driving car project known as Waymo. In recent years, as AlphaGo demonstrated the powerful learning ability of deep learning, the application of environment perception and driving decision-making algorithms based on deep learning in automatic driving has made it possible for unmanned driving to truly replace human driving. However, in the face of complex road scenes, the safety and reliability problems of autonomous dr...

AI Technical Summary

Problems solved by technology

However, in the face of complex road scenes, the safety and reliability problems of autonomous driving technology have always been the bottleneck restricting the promotion and popularization of driverless cars

However, the Faster R-CNN algorithm also has many shortcomings

For example, Faster R-CNN can only use a single GPU for training. When the number of training samples is huge or the feature extraction network is deepened, it will cause problems such as long training time and insufficient video memory; because the features extracted by the feature extraction network are not fine, resulting in The problem of target missed detection; when encountering complex scenes or targets with occlusion or deformation, the problem of target prediction errors

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