Improved faster R-CNN identification method for containers

Abstract

In a complex port environment, the fast and effective automatic visual recognition of containers is an important part of the intelligent operation and management of ports. Due to the large amount of container image data of complex scale and shape, the traditional target detection and recognition algorithm is limited by the illumination, weather and scenes of the port, it has created challenges and difficulties in port container recognition and identification. This paper proposes a deep learning method for container target recognition detection based on the Faster R-CNN framework, the deep separable network structure is introduced into the VGG network, and the DS-VGG network is designed to improve the accuracy while reducing the network parameters to improve the recognition speed, by introducing the adversarial spatial transformer network (ASTN) to the Faster R-CNN network training to enhance the diversity of data features and improve recognition performance. In order to enhance the convolution feature extraction of container targets, a strategy training network that enhances sample target foreground features, multi-scale training learning and data amplification are used. Finally, the performance test and comparison test of the improved model proposed in this paper are carried out. The test results show that the target recognition speed is 50 frames/s on the container test set, the average accuracy rate is 97.7% and the recall rate is 94.45%. Compared with Faster R-CNN, the recognition performance is significantly improved in complex scenes such as fog, rain and night.