基于级联卷积神经网络的港口多方向舰船检测与分类

doi:10.3969/j.issn.1001-506X.2020.09.04

Abstract

Abstract:

Automatic inshore ship recognition, including target localization and type classification, is an important and challenging problem. However, arbitrarily rotated ships are always moored inshore densely. This makes it very difficult to recognize and locate ship targets. To resolve this problem, a multiclass oriented ship localization and recognition framework is proposed based on a cascade region convolutional neural network (RCNN) and feature designed manually. Firstly, cascade RCNN is adopted to localize and classify the positive regions of ships-a set of bounding boxes (BBox). Secondly, a novel procedure which transforms a bounding box to a rotated bounding box is designed and applied to each BBox. Finally, non-maximum suppression (NMS) is adopted to remove the redundant rotated BBoxes (RBoxes). Extensive experimental results conducted on the dataset collected from Google Earth demonstrate the effectiveness of the proposed approach, compared to the other approaches.

Key words: inshore ship detection, rotated bounding box annotation, ship classification, Canny edge detection, Hough line detection

CLC Number:

TN957.52

Jiachi SUN, Huanxin ZOU, Zhipeng DENG, Meilin LI, Xu CAO, Qian MA. Oriented inshore ship detection and classification based on cascade RCNN[J]. Systems Engineering and Electronics, 2020, 42(9): 1903-1910.

Figures/Tables 12

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类型	1	2	3	4	5	6	7
LWR	4.24	5.10	8.32	8.24	8.45	12.07	5.10
数量	26	65	178	35	205	123	316

目标类型	方法
目标类型	faster RCNN OBB	faster RCNN C-H	Cascade RCNN C-H
第1类	41.18	73.39	88.30
第2类	33.01	74.67	80.89
第3类	30.87	63.15	65.66
第4类	22.31	55.16	60.85
第5类	37.75	49.99	64.30
第6类	73.60	28.94	38.18
第7类	13.66	46.10	44.36
mAP	36.5	55.91	63.22

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Oriented inshore ship detection and classification based on cascade RCNN

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