A Fast Aircraft Detection Method for SAR Images Based on Efficient Bidirectional Path Aggregated Attention Network

Luo, Ru; Chen, Lifu; Jin, Xing; Yuan, Zhihui; Tan, Siyu; Cai, Xingmin; Wang, Jielan

doi:10.3390/rs13152940

Cited by 18 publications

(12 citation statements)

References 26 publications

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“…There are many researches on target detection using deep learning. Chen et al [12,13] inspected the aircraft. Their detection network has a solid ability to extract targets to detect aircraft in the image.…”

Section: Discussionmentioning

confidence: 99%

“…In recent years, deep learning has been widely used in target detection from SAR images. Chen et al [12,13] use multi-scale fusion to enhance the method of extracting features to achieve a high accuracy detection of bridges and aircrafts. Meng et al [14] propose a human pose segmentation algorithm based on deep Convolutional Neural Network (CNN) detection, which enables human images to be divided into several parts for recognition.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Suspicious Multi-Object Detection and Recognition Method for Millimeter Wave SAR Security Inspection Images Based on Multi-Path Extraction Network

Yuan

Zhang

et al. 2021

Remote Sensing

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There are several major challenges in detecting and recognizing multiple hidden objects from millimeter wave SAR security inspection images: inconsistent clarity of objects, similar objects, and complex background interference. To address these problems, a suspicious multi-object detection and recognition method based on the Multi-Path Extraction Network (MPEN) is proposed. In MPEN, You Only Look Once (YOLO) v3 is used as the base network, and then the Multi-Path Feature Pyramid (MPFP) module and modified residual block distribution are proposed. MPFP is designed to output the deep network feature layers separately. Then, to distinguish similar objects more easily, the residual block distribution is modified to improve the ability of the shallow network to capture details. To verify the effectiveness of the proposed method, the millimeter wave SAR images from the laboratory’s self-developed security inspection system are utilized in conducting research on multi-object detection and recognition. The detection rate (probability of detecting a target) and average false alarm (probability of error detection) rate of our method on the target are 94.6% and 14.6%, respectively. The mean Average Precision (mAP) of recognizing multi-object is 82.39%. Compared with YOLOv3, our method shows a better performance in detecting and recognizing similar targets.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Suspicious Multi-Object Detection and Recognition Method for Millimeter Wave SAR Security Inspection Images Based on Multi-Path Extraction Network

Yuan

Zhang

et al. 2021

Remote Sensing

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“…A black image (e.g., all pixel values are zero) with the same size as the input image is selected as the baseline in this paper to obtain the local observation attributions output by the network. Then, the positive attribution (PA) and the positive attribution proportion (PAP) of the feature map in the last three stages of the backbone network are calculated, as shown in Equations ( 2) and (3). Combining the values of PA and PAP, the detection performance analysis of the network on the input samples can be obtained.…”

Section: Integrated Gradientsmentioning

confidence: 99%

“…With the improving resolution of acquired SAR images, aircraft detection is beginning to be more widely-adopted in advanced image analytics studies [2]. The challenge of aircraft detection lies in the increasing data volume, the interference of complex backgrounds, and scattered image features of aircraft as objects for detection [3]. Among various SAR image analytical methods, machine learning approaches have attracted considerable interest due to their high accuracy and ability to automatically process large volumes of SAR imagery [4].…”

Section: Introductionmentioning

confidence: 99%

Glassboxing Deep Learning to Enhance Aircraft Detection from SAR Imagery

et al. 2021

Self Cite

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Although deep learning has achieved great success in aircraft detection from SAR imagery, its blackbox behavior has been criticized for low comprehensibility and interpretability. Such challenges have impeded the trustworthiness and wide application of deep learning techniques in SAR image analytics. In this paper, we propose an innovative eXplainable Artificial Intelligence (XAI) framework to glassbox deep neural networks (DNN) by using aircraft detection as a case study. This framework is composed of three parts: hybrid global attribution mapping (HGAM) for backbone network selection, path aggregation network (PANet), and class-specific confidence scores mapping (CCSM) for visualization of the detector. HGAM integrates the local and global XAI techniques to evaluate the effectiveness of DNN feature extraction; PANet provides advanced feature fusion to generate multi-scale prediction feature maps; while CCSM relies on visualization methods to examine the detection performance with given DNN and input SAR images. This framework can select the optimal backbone DNN for aircraft detection and map the detection performance for better understanding of the DNN. We verify its effectiveness with experiments using Gaofen-3 imagery. Our XAI framework offers an explainable approach to design, develop, and deploy DNN for SAR image analytics.

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“…Wu et al [ 14 ] enhanced the detection effect by adding improved self-calibrated convolution and dilated convolution into the Mask R-CNN framework. Luo et al [ 15 ] proposed the Involution Enhanced Path Aggregation (IEPA) module and Effective Residual Shuffle Attention (ERSA) module, which were systematically integrated into the YOLOv5 base network to improve the aircraft detection accuracy.…”

Section: Introductionmentioning

confidence: 99%

A Novel Method of Aircraft Detection under Complex Background Based on Circular Intensity Filter and Rotation Invariant Feature

Chen

Liu

et al. 2022

Sensors

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Aircraft detection in remote sensing images (RSIs) has drawn widespread attention in recent years, which has been widely used in the military and civilian fields. While the complex background, variations of aircraft pose and size bring great difficulties to the effective detection. In this paper, we propose a novel aircraft target detection scheme based on small training samples. The scheme is coarse-to-fine, which consists of two main stages: region proposal and target identification. First, in the region proposal stage, a circular intensity filter, which is designed based on the characteristics of the aircraft target, can quickly locate the centers of multi-scale suspicious aircraft targets in the RSIs pyramid. Then the target regions can be extracted by adding bounding boxes. This step can get high-quality but few candidate regions. Second, in the stage of target identification, we proposed a novel rotation-invariant feature, which combines rotation-invariant histogram of oriented gradient and vector of locally aggregated descriptors (VLAD). The feature can characterize the aircraft target well by avoiding the impact of its rotation and can be effectively used to remove false alarms. Experiments are conducted on Remote Sensing Object Detection (RSOD) dataset to compare the proposed method with other advanced methods. The results show that the proposed method can quickly and accurately detect aircraft targets in RSIs and achieve a better performance.

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A Fast Aircraft Detection Method for SAR Images Based on Efficient Bidirectional Path Aggregated Attention Network

Cited by 18 publications

References 26 publications

A Suspicious Multi-Object Detection and Recognition Method for Millimeter Wave SAR Security Inspection Images Based on Multi-Path Extraction Network

A Suspicious Multi-Object Detection and Recognition Method for Millimeter Wave SAR Security Inspection Images Based on Multi-Path Extraction Network

Glassboxing Deep Learning to Enhance Aircraft Detection from SAR Imagery

A Novel Method of Aircraft Detection under Complex Background Based on Circular Intensity Filter and Rotation Invariant Feature

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