Prof. Tao Ning
College of Computer Science and Engineering, Dalian Minzu University, China
Title: Vehicle Detection Method with low-carbon Technology in Haze Weather Based on Deep Neural Network
Abstract: Vehicle detection based on deep learning achieves excellent results in normal environments, but it is still challenging to detect objects in low-quality images obtained in hazy weather. Existing methods tend to ignore favorable latent information and it is difficult to balance speed and accuracy rate, etc. Therefore, the existing deep neural network is studied, and the YOLOv5 algorithm is improved based on ResNet. Aiming at the problem of low utilization of shallow features, DensNet is added in the feature extraction stage to reduce feature loss and increase utilization. An attention module is added in the feature extraction and fusion stage to better focus on potential information and improve the detection accuracy in haze weather. In view of the difficulty of vehicle detection in haze weather, Focal loss is introduced to give more weights to difficult samples, balance the number of difficult and easy samples, and improve detection accuracy. The experimental results show that the improved network enhances the accuracy of vehicle recognition and verifies the effectiveness of the method.
Tao Ning, professor of College of Computer Science and Engineering, Dalian Minzu University. He was selected into the Liaoning Province Hundred Thousand Talents Project, Dalian High-level Talent, Special Researcher of China Society of Logistics, and Senior Member of China Computer Federation. He graduated from Dalian Maritime University in July 2013 and obtained a doctorate degree in traffic information engineering and control. From 2016 to 2019, he did postdoctoral research at Dalian University of Technology. He is currently a professor at the College of Computer Science and Engineering, Dalian Minzu University, and the ideological and political director of the National Ethnic Affairs Commission Key Laboratory of Big Data Application Technology