SeaShips: A Large-Scale Precisely Annotated Dataset for Ship Detection

Shao, Zhenfeng; Wu, Wenjing; Wang, Zhongyuan; Du, Wan; Li, Chengyuan

doi:10.1109/tmm.2018.2865686

Cited by 299 publications

(136 citation statements)

References 33 publications

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“…Seaship Dataset [10] представляет собой набор из 31455 изображений, содержащих шесть основных типов морских судов (перевозчик руды, насыпной грузовоз, грузовой корабль, контейнеровоз, рыболовное судно и пассажирское судно). Изображения представляют собой нарезку некоторого числа видеозаписей, снятых с камер, расположенных на берегу реки.…”

Section: The Article Explores Modern Neural Network Architectures Forunclassified

Realtime Neural Network Algorithm for Full-Frame Marine Surface Objects Recognition

Tupikov¹,

Pavlova²,

Бондаренко³

et al. 2020

IZVESTIYA SFedU. ENGINEERING SCIENCES

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Section: The Article Explores Modern Neural Network Architectures Forunclassified

Realtime Neural Network Algorithm for Full-Frame Marine Surface Objects Recognition

Tupikov¹,

Pavlova²,

Бондаренко³

et al. 2020

IZVESTIYA SFedU. ENGINEERING SCIENCES

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“…In 2018, Shao et al built a new large-scale dataset of ships, which is designed for training and evaluating ship object detection algorithms. e dataset currently consists of 31455 images and covers six common ship types [31]. In 2019, Shao et al proposed to use visual images captured by an on-land surveillance camera network to achieve real-time detection based on a saliency-aware CNN framework [32].…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Ship Image/Video Detection and Classification Method with Improved Regressive Deep Convolutional Neural Network

et al. 2020

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The shipping industry is developing towards intelligence rapidly. An accurate and fast method for ship image/video detection and classification is of great significance for not only the port management, but also the safe driving of Unmanned Surface Vehicle (USV). Thus, this paper makes a self-built dataset for the ship image/video detection and classification, and its method based on an improved regressive deep convolutional neural network is presented. This method promotes the regressive convolutional neural network from four aspects. First, the feature extraction layer is lightweighted by referring to YOLOv2. Second, a new feature pyramid network layer is designed by improving its structure in YOLOv3. Third, a proper frame and scale suitable for ships are designed with a clustering algorithm to reduced 60% anchors. Last, the activation function is verified and optimized. Then, the detecting experiment on 7 types of ships shows that the proposed method has advantage compared with the YOLO series networks and other intelligent methods. This method can solve the problem of low recognition rate and real-time performance for ship image/video detection and classification with a small dataset. On the testing-set, the final mAP is 0.9209, the Recall is 0.9818, the AIOU is 0.7991, and the FPS is 78–80 in video detection. Thus, this method provides a highly accurate and real-time ship detection method for the intelligent port management and visual processing of the USV. In addition, the proposed regressive deep convolutional network also has a better comprehensive performance than that of YOLOv2/v3.

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“…A detector can tell people the classes and locations of instances in an image. Object detection has a wide range of applications, including face detection [1], pedestrian detection [2], ship detection [3].…”

Section: Introductionmentioning

confidence: 99%

Auxiliary Detection Head for One-Stage Object Detection

Jin

Taniguchi

2020

IEEE Access

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The auxiliary classifier can improve the performance of classification networks. However, the utility of the auxiliary detection head has not been explored in the object detection field. In this paper, we propose an auxiliary detection head to boost the performance of one-stage object detectors. Similar to other detection heads, the auxiliary detection head consists of a classification subnet and a regression subnet, which are essentially two convolution layers. Thus the auxiliary detection head is computationally efficient. Besides, the auxiliary detection head achieves implicit two-step cascaded regression. Specifically, the auxiliary detection head uses its output boxes as anchors for further regression. Within the auxiliary detection head, refinement of object localization corresponds to adjust the positions of its output boxes towards ground truth boxes, which helps the network learn more robust features. At inference, the auxiliary detection head can be removed without any adverse effect on the performance of the main detector head, which benefits from its independence and leads to two advantages: shrink the model size and shorten inference time. The proposed method is evaluated on Pascal VOC and COCO datasets. By incorporating the auxiliary detection head into a state-of-the-art object detector in parallel with the main detection head, we show consistent improvement over its performance on different benchmarks, whereas no extra parameters are introduced at inference time. INDEX TERMS Auxiliary detection head, two-step cascaded regression, convolutional neural networks, object detection. The associate editor coordinating the review of this manuscript and approving it for publication was Wei Liu.

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SeaShips: A Large-Scale Precisely Annotated Dataset for Ship Detection

Cited by 299 publications

References 33 publications

Realtime Neural Network Algorithm for Full-Frame Marine Surface Objects Recognition

Realtime Neural Network Algorithm for Full-Frame Marine Surface Objects Recognition

An Intelligent Ship Image/Video Detection and Classification Method with Improved Regressive Deep Convolutional Neural Network

Auxiliary Detection Head for One-Stage Object Detection

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