Ship Target Detection Algorithm Based on Improved Faster R-CNN

• Main Authors: Liang Qi, Bangyu Li, Liankai Chen, Wei Wang, Liang Dong, Xuan Jia, Jing Huang, Chengwei Ge, Ganmin Xue, Dong Wang
• Format: Article
• Language: English
• Published: MDPI AG 2019-08-01
• Series: Electronics
• Subjects: ship target detection; Faster R-CNN; scene semantic narrowing; topic narrowing subnetwork
• Online Access: https://www.mdpi.com/2079-9292/8/9/959

Ship target detection has urgent needs and broad application prospects in military and marine transportation. In order to improve the accuracy and efficiency of the ship target detection, an improved Faster R-CNN (Faster Region-based Convolutional Neural Network) algorithm of ship target detection is proposed. In the proposed method, the image downscaling method is used to enhance the useful information of the ship image. The scene narrowing technique is used to construct the target regional positioning network and the Faster R-CNN convolutional neural network into a hierarchical narrowing network, aiming at reducing the target detection search scale and improving the computational speed of Faster R-CNN. Furthermore, deep cooperation between main network and subnet is realized to optimize network parameters after researching Faster R-CNN with subject narrowing function and selecting texture features and spatial difference features as narrowed sub-networks. The experimental results show that the proposed method can significantly shorten the detection time of the algorithm while improving the detection accuracy of Faster R-CNN algorithm.