Improved YOLOv4 Based on Attention Mechanism for Ship Detection in SAR Images

Yunlong Gao,Chuan Wu,Ming Ren,Zhiyong Wu

doi:10.1109/access.2022.3154474

Abstract

Ship detection in synthetic aperture radar (SAR) images is an important and challenging work in the field of image processing. Traditional detection algorithms usually rely on handmade features or predefined thresholds, the different performance is obtained with varying degrees of prior knowledge, and it is difficult to take advantage of big data. Recently, deep learning algorithms have found wide applications in ship detection from SAR images. However, due to the complex backgrounds and multiscale ships, it is hard for deep networks to extract representative target features, which limits the ship detection performance to a certain extent. In order to tackle the above problems, we propose an improved YOLOv4 (ImYOLOv4) based on attention mechanism. Firstly, to achieve the best trade-off between detection accuracy and speed, we adopt the off-the-shelf YOLOv4 as our basic framework because of its fast detection speed. Secondly, a thresholding attention module (TAM) is introduced to suppress the adverse effect of complex backgrounds and noises. Besides, we embed channel attention module (CAM) into improved BiFPN as the feature pyramid network (FPN) to better enhance the discrimination of the multiscale target features. Finally, the decoupled head with two parallel branches improves the performance of classification and regression. The proposed method is evaluated on public SAR dataset and the experimental results demonstrate that it has higher efficiency and feasibility than other mainstream methods, yielding the accuracy of 94.16% at intersection over union of 0.5 and 58.19% at intersection over union of 0.75.

Highlights

With the continuous improvement of space remote sensing imaging technology, high-resolution and wide-scale remote sensing images are becoming more and more enriched and facilitate a large range of applications
As we can see from the results, adding the parameters brings the improvements in both AP50 and AP75 compared with condition when r = 1 and α = 0
The reduction parameter r avoids overfitting caused by too many training parameters to a certain extent, and α expands the values of the activation function in the part of less than the thresholding -μ, which further demonstrates that avoiding neurons being dead is more important than obtaining sparsity

Summary

Introduction

With the continuous improvement of space remote sensing imaging technology, high-resolution and wide-scale remote sensing images are becoming more and more enriched and facilitate a large range of applications. Remote sensing applications make remote sensing images into plug and play products, which are widely used in all aspects of social and economic life, such as traffic control [1]-[2], geological and mineral exploration [3], environment monitoring [4], and urban construction [5]. Many researches in this field have prioritized synthetic aperture radar (SAR) images and ship detection in SAR images has become one of the most important remote sensing applications [11]-[16]. SAR is an active microwave remote sensing imaging sensor, which has the all-day and allweather surveillance capabilities, making it possible to continuously monitor targets at sea [17]-[20]. It is very important to study the ship detection in SAR images

Methods

Results

Conclusion