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
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.