Ship Detection under Complex Backgrounds Based on Accurate Rotated Anchor Boxes from Paired Semantic Segmentation

Abstract

It is still challenging to effectively detect ship objects in optical remote-sensing images with complex backgrounds. Many current CNN-based one-stage and two-stage detection methods usually first predefine a series of anchors with various scales, aspect ratios and angles, and then the detection results can be outputted by performing once or twice classification and bounding box regression for predefined anchors. However, most of the defined anchors have relatively low accuracy, and are useless for the following classification and regression. In addition, the preset anchors are not robust to produce good performance for other different detection datasets. To avoid the above problems, in this paper we design a paired semantic segmentation network to generate more accurate rotated anchors with smaller numbers. Specifically, the paired segmentation network predicts four parts (i.e., top-left, bottom-right, top-right, and bottom-left parts) of ships. By combining paired top-left and bottom-right parts (or top-right and bottom-left parts), we can take the minimum bounding box of these two parts as the rotated anchor. This way can be more robust to different ship datasets, and the generated anchors are more accurate and have fewer numbers. Furthermore, to effectively use fine-scale detail information and coarse-scale semantic information, we use the magnified convolutional features to classify and regress the generated rotated anchors. Meanwhile, the horizontal minimum bounding box of the rotated anchor is also used to combine more context information. We compare the proposed algorithm with state-of-the-art object-detection methods for natural images and ship-detection methods, and demonstrate the superiority of our method.

Arbitrary-Oriented Inshore Ship Detection based on Multi-Scale Feature Fusion and Contextual Pooling on Rotation Region Proposals

“…However, the authors declare that this framework using rotation region proposals has a defect of higher false alarms. Zhou et al [28,29] propose approaches to use semantic segmentation networks to recognize parts of ships for rotated region proposal, where additional semantic pixel labels of ship parts have to be provided. On the basis of different RPN design to produce rotated bounding boxes, many approaches are being used to improve the performance of inshore ship detection.…”

“…On the basis of different RPN design to produce rotated bounding boxes, many approaches are being used to improve the performance of inshore ship detection. As far as we know, very few studies, except for [6,28], have simultaneously employed a multi-scale feature, rotation region proposal network and contextual pooling in one uniform end-to-end model.…”

“…Yang et al, integrate a dense feature pyramid network, RRPN and Fast R-CNN to inclined results [6]. Zhou et al, use a semantic segmentation network to recognize part of ships for rotated region proposals [28,29]. Although methods adopting a rotation bounding box have already been proposed during the past two years, the problem of accurate rotated region proposal and the consequent information loss of employing rotated boxes are not yet well solved.…”

Change Detection of Deforestation in the Brazilian Amazon Using Landsat Data and Convolutional Neural Networks

“…Deep learning (DL) has recently attracted increasing attention from remote sensing researchers because of its ability to automatically extract features from the image dataset, high-level semantic segmentation, nonlinear problem modeling, and mapping in complex environments [21]. DL has shown great potential in remote sensing, producing state-of-the-art results in different types of remote sensing data processing [22]: image registration [23][24][25][26], land-use and land-cover classification [27][28][29][30], object detection [31][32][33][34], image fusion [35][36][37][38], semantic segmentation [39][40][41][42], and precision evaluation [43]. DL has also been used for change detection techniques, showing superior performance with greater precision in comparison to classic ML methods [44].…”

HQ-ISNet: High-Quality Instance Segmentation for Remote Sensing Imagery

“…An et al [12] came up with a DRBox-v2 with rotatable boxes to boost the precision and recall rates of detection for object detection in HR SAR images. Xiao et al [13] came up with a novel anchor generation algorithm to eliminate the deficiencies in the previous anchor-based detectors. However, these detection results with the bounding boxes and the rotational bounding boxes do not reflect the pixel-level contours of the original targets.…”