Learning Deep Ship Detector in SAR Images From Scratch

Deng, Zhipeng; Sun, Hao; Zhou, Shilin; Zhao, Jiewen

doi:10.1109/tgrs.2018.2889353

Cited by 108 publications

(59 citation statements)

References 49 publications

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“…In the DL field, ImageNet pre-training [67] is a normal practice, which has adopted by many other object detectors. Certainly, we can also start training from scratch [68], but it reduces the accuracy by 4% in our experiments. Detailed research of pre-training will be introduced in Section 5.4.2.…”

Section: Training Strategiesmentioning

confidence: 99%

Depthwise Separable Convolution Neural Network for High-Speed SAR Ship Detection

et al. 2019

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As an active microwave imaging sensor for the high-resolution earth observation, synthetic aperture radar (SAR) has been extensively applied in military, agriculture, geology, ecology, oceanography, etc., due to its prominent advantages of all-weather and all-time working capacity. Especially, in the marine field, SAR can provide numerous high-quality services for fishery management, traffic control, sea-ice monitoring, marine environmental protection, etc. Among them, ship detection in SAR images has attracted more and more attention on account of the urgent requirements of maritime rescue and military strategy formulation. Nowadays, most researches are focusing on improving the ship detection accuracy, while the detection speed is frequently neglected, regardless of traditional feature extraction methods or modern deep learning (DL) methods. However, the high-speed SAR ship detection is of great practical value, because it can provide real-time maritime disaster rescue and emergency military planning. Therefore, in order to address this problem, we proposed a novel high-speed SAR ship detection approach by mainly using depthwise separable convolution neural network (DS-CNN). In this approach, we integrated multi-scale detection mechanism, concatenation mechanism and anchor box mechanism to establish a brand-new light-weight network architecture for the high-speed SAR ship detection. We used DS-CNN, which consists of a depthwise convolution (D-Conv2D) and a pointwise convolution (P-Conv2D), to substitute for the conventional convolution neural network (C-CNN). In this way, the number of network parameters gets obviously decreased, and the ship detection speed gets dramatically improved. We experimented on an open SAR ship detection dataset (SSDD) to validate the correctness and feasibility of the proposed method. To verify the strong migration capacity of our method, we also carried out actual ship detection on a wide-region large-size Sentinel-1 SAR image. Ultimately, under the same hardware platform with NVIDIA RTX2080Ti GPU, the experimental results indicated that the ship detection speed of our proposed method is faster than other methods, meanwhile the detection accuracy is only lightly sacrificed compared with the state-of-art object detectors. Our method has great application value in real-time maritime disaster rescue and emergency military planning.

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Section: Training Strategiesmentioning

confidence: 99%

Depthwise Separable Convolution Neural Network for High-Speed SAR Ship Detection

et al. 2019

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“…The widely used deep learning model has greatly accelerated the research progress in various fields, especially in the fields of computer version [27,28] and natural language processing (NLP) [29,30]. Inspired by this, some mainstream deep learning models are also applied to taxi demand prediction.…”

Section: Deep-learning-based Methodsmentioning

confidence: 99%

Incorporating Graph Attention and Recurrent Architectures for City-Wide Taxi Demand Prediction

2019

IJGI

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Taxi demand prediction is one of the key factors in making online taxi hailing services more successful and more popular. Accurate taxi demand prediction can bring various advantages including, but not limited to, enhancing user experience, increasing taxi utilization, and optimizing traffic efficiency. However, the task is challenging because of complex spatial and temporal dependencies of taxi demand. In addition, relationships between non-adjacent regions are also critical for accurate taxi demand prediction, whereas they are largely ignored by existing approaches. To this end, we propose a novel graph and time-series learning model for city-wide taxi demand prediction in this paper. It has two main building blocks, the first one utilize a graph network with attention mechanism to effectively learn spatial dependencies of taxi demand in a broader perspective of the entire city, and the output at each time interval is then transferred to the second block. In the graph network, the edge is defined by an Origin–Destination relation to capture non-adjacent impacts. The second one uses a neural network which is adept with processing sequence data to capture the temporal correlations of city-wide taxi demand. Using a large, real-world dataset and three metrics, we conduct an extensive experimental study and find that our model outperforms state-of-the-art baselines by 9.3% in terms of the root-mean-square error.

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“…Regions with > 3 pixels are seen as ships, instead of AIS and Google Earth, so label errors may occur declining dataset authenticity. So far, scholars have proposed many SAR ship detection methods [1][2][3][4][5][6]8,[13][14][15][16][17][18][19][20][21]29,32] on SSDD, but SSDD has careless annotation, repeated scenes, deficiency of sample number, etc., which hinders the further progress of research.…”

Section: Ssddmentioning

confidence: 99%

LS-SSDD-v1.0: A Deep Learning Dataset Dedicated to Small Ship Detection from Large-Scale Sentinel-1 SAR Images

Zhang

et al. 2020

Remote Sensing

174

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Ship detection in synthetic aperture radar (SAR) images is becoming a research hotspot. In recent years, as the rise of artificial intelligence, deep learning has almost dominated SAR ship detection community for its higher accuracy, faster speed, less human intervention, etc. However, today, there is still a lack of a reliable deep learning SAR ship detection dataset that can meet the practical migration application of ship detection in large-scene space-borne SAR images. Thus, to solve this problem, this paper releases a Large-Scale SAR Ship Detection Dataset-v1.0 (LS-SSDD-v1.0) from Sentinel-1, for small ship detection under large-scale backgrounds. LS-SSDD-v1.0 contains 15 large-scale SAR images whose ground truths are correctly labeled by SAR experts by drawing support from the Automatic Identification System (AIS) and Google Earth. To facilitate network training, the large-scale images are directly cut into 9000 sub-images without bells and whistles, providing convenience for subsequent detection result presentation in large-scale SAR images. Notably, LS-SSDD-v1.0 has five advantages: (1) large-scale backgrounds, (2) small ship detection, (3) abundant pure backgrounds, (4) fully automatic detection flow, and (5) numerous and standardized research baselines. Last but not least, combined with the advantage of abundant pure backgrounds, we also propose a Pure Background Hybrid Training mechanism (PBHT-mechanism) to suppress false alarms of land in large-scale SAR images. Experimental results of ablation study can verify the effectiveness of the PBHT-mechanism. LS-SSDD-v1.0 can inspire related scholars to make extensive research into SAR ship detection methods with engineering application value, which is conducive to the progress of SAR intelligent interpretation technology. LS-SSDD-v1.0 is available at https://github.com/TianwenZhang0825/LS-SSDD-v1.0-OPEN.

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Learning Deep Ship Detector in SAR Images From Scratch

Cited by 108 publications

References 49 publications

Depthwise Separable Convolution Neural Network for High-Speed SAR Ship Detection

Depthwise Separable Convolution Neural Network for High-Speed SAR Ship Detection

Incorporating Graph Attention and Recurrent Architectures for City-Wide Taxi Demand Prediction

LS-SSDD-v1.0: A Deep Learning Dataset Dedicated to Small Ship Detection from Large-Scale Sentinel-1 SAR Images

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