Foggy day vehicle detection method based on deep learning

Abstract

The invention discloses a foggy day vehicle detection method based on deep learning. The method comprises the following steps: carrying out image preprocessing on an acquired foggy traffic vehicle picture; carrying out feature extraction on the preprocessed foggy traffic vehicle picture by adopting a deep residual network model; obtaining a plurality of feature maps with different sizes, carryingout multi-scale detection on a plurality of feature maps with different sizes to obtain a multi-scale detection feature map and improve feature extraction accuracy; and finally, training the deep residual error network model by adopting a transfer learning method according to the obtained multi-scale detection feature map to obtain a vehicle detection network model in foggy days. A network structure is simplified by adopting a transfer learning method; the method is advantaged in that not only can detection speed be improved, but also target detection precision is improved, clustering is carried out by utilizing the K-means clustering method, the size of the initial prior frame required by the network is acquired, deepening of the shallow network and simplification of the integral framework are carried out, detection speed is improved, the loss function and the predicted output tensor are simplified, and positioning efficiency is improved.

Description

technical field [0001] The invention belongs to the technical field of traffic vehicle detection, and in particular relates to a vehicle detection method in foggy weather based on deep learning. Background technique [0002] With the development of the economy and the prosperity of the automobile manufacturing industry, the number of vehicles is increasing day by day. While bringing convenience to people's life, it also brings serious hidden dangers to traffic safety. At the same time, the progress of industry has led to the continuous increase of smog weather. Under smog weather, visibility is reduced, and the driver's vision becomes blurred, which is likely to cause traffic accidents. At the same time, the speed of vehicles generally decreases in foggy weather, which will cause traffic jams. Therefore, it is of great significance to conduct research on the problem of vehicle detection in haze weather, provide better vehicle detection methods, formulate traffic guidance po...

AI Technical Summary

Problems solved by technology

Subsequently, yolov2 and yolov3 improved the detection accuracy and enhanced the detection speed. However, when the YOLOv3 network identifies small targets and single-type targets, the original network architecture is too redundant, especially in the case of blurred image features. poor result

Images