Effective Vehicle Detection Using Improved Faster Recursive Convolutional Neural Network Model

• Published in: SN computer science 2022-12, Vol.4 (2), p.105, Article 105
• Main Authors: Mahendra, G., Roopashree, H. R.
• Format: Article
• Language: English
• Subjects: Accuracy; Advances in Computational Intelligence; Algorithms; Artificial neural networks; Computer Imaging; Computer Science; Computer Systems Organization and Communication Networks; Data augmentation; Data Structures and Information Theory; Datasets; Deep learning; Information Systems and Communication Service; Intelligent transportation systems; Machine learning; Neural networks; Original Research; Paradigms and Applications; Pattern Recognition and Graphics; Software Engineering/Programming and Operating Systems; Surveillance; Transportation networks; Vehicles; Vision; Visual discrimination
• ISSN: 2661-8907; 2662-995X; 2661-8907
• DOI: 10.1007/s42979-022-01511-4

In recent decades, vehicle recognition plays an essential and effective role in the intelligent transportation system and traffic safety. Currently, the deep learning approaches made an effective impact in the fast vehicle detection application. In the real-time traffic monitoring video sequences, it is difficult to recognize the smaller vehicle targets and multi-scale vehicle targets in the complex scenes. A new fully automated vehicle detection model is implemented in this manuscript to address the above-mentioned issue. After obtaining the videos from KITTI dataset, the mask is created for specific classes like car, pedestrian, and cyclist. Additionally, the data augmentation is accomplished using the techniques like zoom-out, zoom-in, shift, shear, flipping, and rotation. The data augmentation enhances the performance of the deep learning models by creating different and new examples for training the dataset. The deep learning models perform accurately, if the dataset is rich and sufficient. After data augmentation, an improved faster Recursive Convolutional Neural Network (R-CNN) model is developed for vehicle detection. The improved faster R-CNN model first extracts discriminative feature values from the images utilizing U-Net and Visual Geometry Group (VGG) 19 pre-trained methods. Then, it creates the region proposal to improve the detection performance and narrow the search space. On the KITTI dataset, the improved faster R-CNN model achieved 90.59% of average precision and 0.45 s of processing time, which are better compared to the existing models.