Multiscale Rotated Bounding Box-Based Deep Learning Method for Detecting Ship Targets in Remote Sensing Images

Abstract

Since remote sensing images are captured from the top of the target, such as from a satellite or plane platform, ship targets can be presented at any orientation. When detecting ship targets using horizontal bounding boxes, there will be background clutter in the box. This clutter makes it harder to detect the ship and find its precise location, especially when the targets are in close proximity or staying close to the shore. To solve these problems, this paper proposes a deep learning algorithm using a multiscale rotated bounding box to detect the ship target in a complex background and obtain the location and orientation information of the ship. When labeling the oriented targets, we use the five-parameter method to ensure that the box shape is maintained rectangular. The algorithm uses a pretrained deep network to extract features and produces two divided flow paths to output the result. One flow path predicts the target class, while the other predicts the location and angle information. In the training stage, we match the prior multiscale rotated bounding boxes to the ground-truth bounding boxes to obtain the positive sample information and use it to train the deep learning model. When matching the rotated bounding boxes, we narrow down the selection scope to reduce the amount of calculation. In the testing stage, we use the trained model to predict and obtain the final result after comparing with the score threshold and nonmaximum suppression post-processing. Experiments conducted on a remote sensing dataset show that the algorithm is robust in detecting ship targets under complex conditions, such as wave clutter background, target in close proximity, ship close to the shore, and multiscale varieties. Compared to other algorithms, our algorithm not only exhibits better performance in ship detection but also obtains the precise location and orientation information of the ship.

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

“…Liu et al [27] bring in a rotated region CNN and rotation ROI pooling to detect inclined ships, but the skew proposals are generated by the method like a selective search presented by [11] (not end-to-end model). Li et al [36] employ a five-box method to produce rotation proposals, which is not a region proposal network. In order to build an end-to-end model, Zhang et al, introduce a rotation region proposal network instead of the above rotation proposal generation methods to generate rotated bounding boxes [37].…”

Towards Automated Ship Detection and Category Recognition from High-Resolution Aerial Images

“…The zero means generating the horizontal anchors, and the other two generate oriented anchors. In addition, as ships always have special shapes, we analyzed the shape of the ship targets in the dataset and refer to the setting of the relevant work [18,[34][35][36][37]. We set 1:3, 1:7 as the aspect ratios.…”

Adaptive Saliency Biased Loss for Object Detection in Aerial Images

“…Here is the problem: How to get better results for aerial images using object detection algorithms while decreasing latency speed? Based on the properties of aerial images, recent work demonstrates rotation-based [22], [23] and multiscale-based networks [? ], [22] which focus on solving the first two challenges found in aerial images.…”

“…Based on the properties of aerial images, recent work demonstrates rotation-based [22], [23] and multiscale-based networks [? ], [22] which focus on solving the first two challenges found in aerial images. These models demonstrate significant improvements in aerial image object detection.…”