Achievement of Small Target Detection for Sea Ship Based on CFAR-DBN

• Published in: Wireless communications and mobile computing Vol. 2022; pp. 1 - 8
• Main Authors: Yuan, LiBing, Chi, XueLi, Wei, Hui
• Format: Journal Article
• Language: English
• Published: Oxford Hindawi 18.02.2022; Hindawi Limited
• Subjects: Algorithms; Artificial intelligence; Belief networks; Constant false alarm rate; Deep learning; Image detection; Interference; Marine transportation; Neural networks; Robustness; Ships; Signal processing; Target detection
• ISSN: 1530-8669; 1530-8677
• DOI: 10.1155/2022/4630155

With the rapid development of marine exploration and marine transportation, the activities of marine ships are becoming more and more frequent. Accurate and rapid detection of the position of marine ships has very important practical and strategic significance. SAR has the characteristics of all-weather detection. It is an important means of ship detection. Aiming at the problem of fuzzy interference in sea surface ship SAR image detection, a small target image detection algorithm based on constant false alarm rate (CFAR) and depth belief network (DBN) is proposed. Firstly, according to the traditional CFAR detection principle, the whole image to be detected is detected by CFAR globally, and the index matrix is obtained, so as to improve the ship detection speed. Secondly, the output data of the hidden layer of the last layer of DBN are used as the input data of SVM, and the trained DBN model is applied to local detection, so as to improve the accuracy and robustness of ship detection. Finally, the algorithm combining CFAR and DBN is trained and applied in ship detection. Experimental results show that the accuracy of the proposed algorithm under fuzzy interference is better than that of traditional CFAR, BPNN, Fast R-CNN, and SSD512 algorithms, which proves that the robustness of the combined algorithm is significantly improved.