An Improved Deep Network-Based Scene Classification Method for Self-Driving Cars

• Published in: IEEE transactions on instrumentation and measurement Vol. 71; pp. 1 - 14
• Main Authors: Ni, Jianjun, Shen, Kang, Chen, Yinan, Cao, Weidong, Yang, Simon X.
• Format: Journal Article
• Language: English
• Published: New York IEEE 2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural networks; Automobiles; Autonomous automobiles; Autonomous cars; Autonomous vehicles; Classification; Datasets; Decision making; Deep network; faster region with convolutional neural network features (RCNN); Feature extraction; feature fusion; Image recognition; Intelligent transportation systems; Machine learning; Object recognition; Redundancy; Roads; scene classification; self-driving car; Semantics; Traffic congestion; Traffic jams; Transportation networks; Visualization
• DOI: 10.1109/TIM.2022.3146923
• EISSN: 1557-9662
• ISSN: 0018-9456

A self-driving car is a hot research topic in the field of the intelligent transportation system, which can greatly alleviate traffic jams and improve travel efficiency. Scene classification is one of the key technologies of self-driving cars, which can provide the basis for decision-making in self-driving cars. In recent years, deep learning-based solutions have achieved good results in the problem of scene classification. However, some problems should be further studied in the scene classification methods, such as how to deal with the similarities among different categories and the differences among the same category. To deal with these problems, an improved deep network-based scene classification method is proposed in this article. In the proposed method, an improved faster region with convolutional neural network features (RCNN) network is used to extract the features of representative objects in the scene to obtain local features, where a new residual attention block is added to the Faster RCNN network to highlight local semantics related to driving scenarios. In addition, an improved Inception module is used to extract global features, where a mixed Leaky ReLU and ELU function is presented, to reduce the possible redundancy of the convolution kernel and enhance the robustness. Then, the local features and the global features are fused to realize the scene classification. Finally, a private dataset is built from the public datasets for the specialized application of scene classification in the self-driving field, and the proposed method is tested on the proposed dataset. The experimental results show that the accuracy of the proposed method can reach 94.76%, which is higher than the state-of-the-art methods.