Emergency lane vehicle detection and classification method based on logistic regression and a deep convolutional network

With the ever-improving degree of car privatization, the number of car-owning residents has repeatedly reached new highs, but it has also caused traffic congestion, especially lane congestion. To reduce emergency lane congestion, and realize the intelligent detection and classification of emergency lanes, this paper introduces logistic regression theory and proposes a vehicle detection method based on logistic regression. Based on the powerful feature abstraction and automatic learning capabilities of convolutional networks, it studies vehicles based on convolutional networks. The recognition algorithm analyses the contribution of the convolution kernel in the convolutional network, and transforms the original emergency lane vehicle detection. The research results show that the emergency lane vehicle detection rate based on logistic regression and a deep convolutional network designed in this paper can reach approximately 98%, which is higher than the detection methods based on SVM and AdaBoost, and the detection time is not different from SVM and AdaBoost. After training, the convolution kernel of the deep convolutional network, the detection time and detection accuracy improved to a certain extent. This shows that the emergency lane vehicle detection and classification method based on logistic regression and a deep convolutional network, can play an important role in emergency lane intelligence.