A multi-target corner pooling-based neural network for vehicle detection

Convolutional neural network has shown strong capability to improve performance in vehicle detection, which is one of the main research topics of intelligent transportation system. Aiming to detect the blocked vehicles efficiently in actual traffic scenes, we propose a novel convolutional neural network based on multi-target corner pooling layers. The hourglass network, which could extract local and global information of the vehicles in the images simultaneously, is chosen as the backbone network to provide vehicles’ features. Instead of using the max pooling layer, the proposed multi-target corner pooling (MTCP) layer is used to generate the vehicles’ corners. And in order to complete the blocked corners that cannot be generated by MTCP, a novel matching corners method is adopted in the network. Therefore, the proposed network can detect blocked vehicles accurately. Experiments demonstrate that the proposed network achieves an AP of 43.5% on MS COCO dataset and a precision of 93.6% on traffic videos, which outperforms the several existing detectors.