Multi-collaborative Regression Convolutional Neural Network for Vehicle Angle Detection

Abstract

Background: The vehicle pose detection plays an important role in monitoring vehicle behavior and the parking situation. The real-time detection of vehicle pose with high accuracy is of great importance. Objective: The goal of the work is to construct a new network to detect the vehicle angle based on the regression Convolutional Neural Network (CNN). The main contribution is that several traditional regression CNNs are combined as the Multi-Collaborative Regression CNN (MCR-CNN), which greatly enhances the vehicle angle detection precision and eliminates the abnormal detection error. Methods: Two challenges with respect to the traditional regression CNN have been revealed in detecting the vehicle pose angle. The first challenge is the detection failure resulting from the conversion of the periodic angle to the linear angle, while the second is the big detection error if the training sample value is very small. An MCR-CNN is proposed to solve the first challenge. And a 2- stage method is proposed to solve the second challenge. The architecture of the MCR-CNN is designed in detail. After the training and testing data sets are constructed, the MCR-CNN is trained and tested for vehicle angle detection. Results: The experimental results show that the testing samples with the error below 4° account for 95% of the total testing samples based on the proposed MCR-CNN. The MCR-CNN has significant advantages over the traditional vehicle pose detection method. Conclusion: The proposed MCR-CNN cannot only detect the vehicle angle in real-time, but also has a very high detection accuracy and robustness. The proposed approach can be used for autonomous vehicles and monitoring of the parking lot.