In:
Journal of Sensors, Hindawi Limited, Vol. 2022 ( 2022-7-20), p. 1-12
Abstract:
With the recent development of deep convolutional neural network (CNN), remote sensing for ship detection methods has achieved enormous progress. However, current methods focus on the whole ships and fail on the component’s detection of a ship. To detect ships from remote-sensing images in a more refined way, we employ the inherent relationship between ships and their critical parts to establish a multilevel structure and propose a novel framework to improve the performance in identifying the multilevel objects. Our framework, named the dual detector network (DD-Net), consists of two carefully designed detectors, one for ships (the ship detector) and the other for their critical parts (the critical part detector), for detecting the critical parts in a coarse-to-fine manner. The ship detector offers detection results of the ship, based on which the critical part detector detects small critical parts inside each ship region. The framework is trained in an end-to-end way by optimizing the multitask loss. Due to the lack of publicly available datasets for critical part detection, we build a new dataset named RS-Ship with 1015 remote-sensing images and 2856 annotations. Experiments on the HRSC2016 dataset and the RS-Ship dataset show that our method performs well in the detection of ships and critical parts.
Type of Medium:
Online Resource
ISSN:
1687-7268
,
1687-725X
DOI:
10.1155/2022/9602100
Language:
English
Publisher:
Hindawi Limited
Publication Date:
2022
detail.hit.zdb_id:
2397931-8
