Convolutional Neural Network Based Weakly Supervised Learning for Aircraft Detection From Remote Sensing Image

Wu, Zhize; Weise, Thomas; Wang, Yan; Wang, Yongjun

doi:10.1109/access.2020.3019956

Cited by 25 publications

(12 citation statements)

References 45 publications

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“…For a RSI with size of MN, the foreground and background are segmented according to the optimal grayscale threshold, which can be evaluated by the OTSU algorithm as follows [31]: {1,2,...,255} arg max ( ( ))…”

Section: Ms-cnn With Attention For Aircraft Detectionmentioning

confidence: 99%

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Aircraft Detection in Remote Sensing Image based on Multi-scale Convolution Neural Network with Attention Mechanism

Wang

Huang

et al. 2021

Preprint

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Detecting aircraft from remote sensing image (RSI) is an important but challenging task due to the variations of aircraft type, size, pose, angle, complex background and small size of aircraft in RSIs. An aircraft detection method is proposed based on multi-scale convolution neural network with attention (MSCNNA), consisting of encoder, decoder, attention and classification. In MSCNNA, the multiple convolutional and pooling kernels with different sizes are utilized to learn the multi-scale discriminant features, and the global attention mechanism (GAM) is employed to capture the spatial and channel dependencies and adaptively preserve the relationships of the entire image. Compared with the standard deep CNN, multi-scale convolution neural networks (CNN) and GAM are integrated to learn the multi-scale features for aircraft detection, especially small aircrafts. Experiment results on the aircraft image dataset of the public EORSSD dataset show that the proposed method outperforms the state-of-the-art method on the same dataset and the obtained multi-size aircraft edge is clearer.

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Section: Ms-cnn With Attention For Aircraft Detectionmentioning

confidence: 99%

“…saliency and CNN (SCNN) [7], Markov random field-FCN (M-FCN) [8], CNN based weakly supervised learning (CNNWSL) [31], CNN based semantic segmentation (CNNSS) [32]. All models are trained on the EORSSD dataset, and the main experimental configuration is listed in Table 1.…”

Section: Experiments and Analysismentioning

confidence: 99%

Aircraft Detection in Remote Sensing Image based on Multi-scale Convolution Neural Network with Attention Mechanism

Wang

Huang

et al. 2021

Preprint

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“…In the aircraft detection of remote sensing images, it is difficult to detect the aircraft due to the small object of the aircraft. Therefore, Wu et al [38] experiment with the improved method on the remote sensing aircraft images constructed by themselves, which results in a lower false-positive rate and shorter training time. Schumann et al [39] propose a new car radar data set, which contains measured values and point-by-point markings from the same car for more than four hours.…”

Section: A Thangkamentioning

confidence: 99%

RPTK1: A New Thangka Data Set for Object Detection of Thangka Images

2021

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Data set is the basis of machine learning, a good data set can promote the development of various applications. Machine learning has been deeply involved in the protection and inheritance of cultural resources. However, there are few data sets about Thangka, and the types and quantity of Thangka images are relatively few. Therefore, we first establish a Thangka data set called RPTK1 (Religious Portrait Thangka Version 1), which contains 3,338 Thangka images, more than any other Thangka data set. The objects in the data set basically cover the common Thangka religious portraits, tools and headdresses, and are marked in the professional language of Buddhism. Then, on the basis of the RPTK1 data set, in order to achieve better detection of small Thangka objects (Thangka religious tools), we propose an improved Single Shot MultiBox Detector (SSD) method, called Single Shot MultiBox Detector with Improvement Feature Fusion And Loss Function (FALSSD). Finally, in order to verify the effectiveness of the FALSSD method, we conduct experiments on the RPTK1 data set. The experimental results show that the mAP of our method in the RPTK1 data set reaches 83.85%. Compared with the other ten state-of-the-art methods, the performance of our model is better.INDEX TERMS Thangka data set, object detection, SSD method, feature fusion.

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“…Li et al [16] aimed at the problem of recognizing aircraft in remote sensing images that contained multiple objects and background; a human-computer fusion framework that combined the advantages of human and computer was proposed. Wu et al [17] aimed at the problem that aircraft targets were usually small and the cost of manual annotation was very high; a simple yet efficient aircraft detection algorithm called Weakly Supervised Learning in AlexNet (AlexNet-WSL) was proposed to know detectors with only image-level annotations. Xu et al [18] aimed at the problem that the aircraft to be detected was very small in optical remote sensing images and the interference of objects to the aircraft had a great impact on the aircraft characteristics in remote sensing images; a multiscale fusion prediction network (MFPN) was proposed to perform feature fusion from multiple angles to achieve a rich combination of gradients.…”

Section: Introductionmentioning

confidence: 99%

Aircraft Detection for Remote Sensing Images Based on Deep Convolutional Neural Networks

Zhou

Yan

Shan

et al. 2021

Journal of Electrical and Computer Engineering

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Aircraft detection for remote sensing images, as one of the fields of computer vision, is one of the significant tasks of image processing based on deep learning. Recently, many high-performance algorithms for aircraft detection have been developed and applied in different scenarios. However, the proposed algorithms still have a series of problems; for instance, the algorithms will miss some small-scale aircrafts when applied to the remote sensing image. There are two main reasons for the problem; one reason is that the aircrafts in the remote sensing image are usually small in size, leading to detecting difficulty. The other reason is that the background of the remote sensing image is usually complex, so the algorithms applied to the scenario are easy to be affected by the background. To address the problem of small size, this paper proposes the Multiscale Detection Network (MSDN) which introduces a multiscale detection architecture to detect small-scale aircrafts. With the intention to resist the background noise, this paper proposes the Deeper and Wider Module (DAWM) which increases the perceptual field of the network to alleviate the affection. Besides, to address the two problems simultaneously, this paper introduces the DAWM into the MSDN and names the novel network structure as Multiscale Refined Detection Network (MSRDN). The experimental results show that the MSRDN method has detected the small-scale aircrafts that other algorithms missed and the performance indicators have higher performance than other algorithms.

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Convolutional Neural Network Based Weakly Supervised Learning for Aircraft Detection From Remote Sensing Image

Cited by 25 publications

References 45 publications

Aircraft Detection in Remote Sensing Image based on Multi-scale Convolution Neural Network with Attention Mechanism

Aircraft Detection in Remote Sensing Image based on Multi-scale Convolution Neural Network with Attention Mechanism

RPTK1: A New Thangka Data Set for Object Detection of Thangka Images

Aircraft Detection for Remote Sensing Images Based on Deep Convolutional Neural Networks

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