Learning Slimming SAR Ship Object Detector Through Network Pruning and Knowledge Distillation

Chen, Shiqi; Zhan, Ronghui; Wang, Wei; Zhang, Jun

doi:10.1109/jstars.2020.3041783

Cited by 77 publications

(34 citation statements)

References 38 publications

(43 reference statements)

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“…This is mostly due to the free availability of some MS satellite images and their wide range of applications. Aerial images [54,135,136], hyperspectral images [137][138][139], and SAR images [97,140,141] were also processed with At-DL methods in 55, 43, and 24 papers, respectively. However, UAV images were used in only three papers [34,94,142].…”

Section: Rq4 What Are the Used Data Sets/types In Attention-based Deep Learning Methods For Remote Sensing Image Processing?mentioning

confidence: 99%

Effect of Attention Mechanism in Deep Learning-Based Remote Sensing Image Processing: A Systematic Literature Review

et al. 2021

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Machine learning, particularly deep learning (DL), has become a central and state-of-the-art method for several computer vision applications and remote sensing (RS) image processing. Researchers are continually trying to improve the performance of the DL methods by developing new architectural designs of the networks and/or developing new techniques, such as attention mechanisms. Since the attention mechanism has been proposed, regardless of its type, it has been increasingly used for diverse RS applications to improve the performances of the existing DL methods. However, these methods are scattered over different studies impeding the selection and application of the feasible approaches. This study provides an overview of the developed attention mechanisms and how to integrate them with different deep learning neural network architectures. In addition, it aims to investigate the effect of the attention mechanism on deep learning-based RS image processing. We identified and analyzed the advances in the corresponding attention mechanism-based deep learning (At-DL) methods. A systematic literature review was performed to identify the trends in publications, publishers, improved DL methods, data types used, attention types used, overall accuracies achieved using At-DL methods, and extracted the current research directions, weaknesses, and open problems to provide insights and recommendations for future studies. For this, five main research questions were formulated to extract the required data and information from the literature. Furthermore, we categorized the papers regarding the addressed RS image processing tasks (e.g., image classification, object detection, and change detection) and discussed the results within each group. In total, 270 papers were retrieved, of which 176 papers were selected according to the defined exclusion criteria for further analysis and detailed review. The results reveal that most of the papers reported an increase in overall accuracy when using the attention mechanism within the DL methods for image classification, image segmentation, change detection, and object detection using remote sensing images.

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Section: Rq4 What Are the Used Data Sets/types In Attention-based Deep Learning Methods For Remote Sensing Image Processing?mentioning

confidence: 99%

Effect of Attention Mechanism in Deep Learning-Based Remote Sensing Image Processing: A Systematic Literature Review

et al. 2021

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“…Zhao et al [50] proposed an attention receptive pyramid network (ARPN), which design a dilated attention block to enhance the relationships among nonlocal features and refine information at different feature maps. Chen et al [51] designed a novel attention mechanism to help the detector focus more on the salient regions containing ships and combined it with knowledge distillation to improve the performance. Yang et al [52] proposed R-RetinaNet to solve the problems such as mismatch of feature scale, contradictions between different learning tasks, and the unbalanced distribution of positive samples.…”

Section: Introductionmentioning

confidence: 99%

An Anchor-Free Detection Method for Ship Targets in High-Resolution SAR Images

Sun

Dai

Leng

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

139

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With the rapid development of earth observation technology, high-resolution synthetic aperture radar (HR SAR) imaging satellites could provide more observational information for maritime surveillance. However, there are still some problems to detect ship targets in HR SAR images due to the complex surroundings, targets defocusing, and diversity of the scales. In this article, an anchor-free method is proposed for ship target detection in HR SAR images. First, fully convolutional one-stage object detection (FCOS) as the base network is applied to detect ship targets, achieving better detection performance through pixel-bypixel prediction of the image. Second, the category-position (CP) module is proposed to optimize the position regression branch features in the FCOS network. This module can improve target positioning performance in complex scenes by generating guidance vector from the classification branch features. At the same time, target classification and boundary box regression methods are redesigned to shield the adverse effects of fuzzy areas in the network training. Finally, to evaluate the effectiveness of CP-FCOS, extensive experiments are conducted on HRSID, SSDD, IEEE 2020 Gaofen Challenge SAR dataset, and two complex largescene HR SAR images. The experimental results show that our method can obtain encouraging detection performance compared with Faster-RCNN, RetinaNet, and FCOS. Remarkably, the proposed method was applied to SAR ship detection in the 2020 Gaofen Challenge. Our team ranked first among 292 teams in the preliminary contest and won seventh place in the final match.

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“…Yolo is also widely used in radar images and remote sensing images. For instance, both Chen et al [ 27 ] and Zhou et al [ 28 ] used radar images as training datasets, and their detectors achieved lighter architecture and real-time detection speed. Gao et al [ 29 ] developed a novel CNN model named YOLO-S- CIOU , which is based on YOLOv3 for specific building detection.…”

Section: Introductionmentioning

confidence: 99%

Improved YOLO Based Detection Algorithm for Floating Debris in Waterway

Lin

Tian

Jin

et al. 2021

Entropy

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Various floating debris in the waterway can be used as one kind of visual index to measure the water quality. The traditional image processing method is difficult to meet the requirements of real-time monitoring of floating debris in the waterway due to the complexity of the environment, such as reflection of sunlight, obstacles of water plants, a large difference between the near and far target scale, and so on. To address these issues, an improved YOLOv5s (FMA-YOLOv5s) algorithm by adding a feature map attention (FMA) layer at the end of the backbone is proposed. The mosaic data augmentation is applied to enhance the detection effect of small targets in training. A data expansion method is introduced to expand the training dataset from 1920 to 4800, which fuses the labeled target objects extracted from the original training dataset and the background images of the clean river surface in the actual scene. The comparisons of accuracy and rapidity of six models of this algorithm are completed. The experiment proves that it meets the standards of real-time object detection.

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Learning Slimming SAR Ship Object Detector Through Network Pruning and Knowledge Distillation

Cited by 77 publications

References 38 publications

Effect of Attention Mechanism in Deep Learning-Based Remote Sensing Image Processing: A Systematic Literature Review

Effect of Attention Mechanism in Deep Learning-Based Remote Sensing Image Processing: A Systematic Literature Review

An Anchor-Free Detection Method for Ship Targets in High-Resolution SAR Images

Improved YOLO Based Detection Algorithm for Floating Debris in Waterway

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