Ship target detection and identification based on SSD_MobilenetV2

Zou, Yiyang; Zhao, Lining; Qin, Shanxing; Pan, Mingyang; Li, Zhelin

doi:10.1109/itoec49072.2020.9141734

Cited by 40 publications

(13 citation statements)

References 10 publications

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“…In the feature fusion module, a new feature pyramid is generated through downsampling and fed to a multi-box detector to predict the result, thereby significantly improving the performance of the SSD without a decrease in speed. Zou et al [35] also proposed the use of a MobilenetV2 network trained in advance on the COCO dataset to extract the features of the ship images and effectively reduce the training time and number of computing resources. Kaiqiang et al [36] proposed the combination of an ordinary convolution with a deeply separable convolution for synchronous feature learning to acquire richer shallow features and then added a space-based weight adaptive network to place more attention on the important features of the space, which can effectively reduce the rate of detection errors for small targets.…”

Section: Related Workmentioning

confidence: 99%

A Study on Small-Scale Ship Detection Based on Attention Mechanism

Zheng

Liu

2022

IEEE Access

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Section: Related Workmentioning

confidence: 99%

A Study on Small-Scale Ship Detection Based on Attention Mechanism

Zheng

Liu

2022

IEEE Access

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“…The deep learning detection methods: with the boom development of deep learning, many ship object detection methods based on deep CNN have been proposed. Zou et al [ 32 ] proposed an improved SSD algorithm based on MobilenetV2 [ 33 ] and finally achieved better detection results in three types of ship images. Zhao et al [ 34 ] proposed a new network architecture based on the Faster R-CNN by using squeeze and excitation for ship detection in SAR images.…”

Section: Related Workmentioning

confidence: 99%

NSD-SSD: A Novel Real-Time Ship Detector Based on Convolutional Neural Network in Surveillance Video

Sun

Liang

2021

Computational Intelligence and Neuroscience

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With the rapid development of the marine industry, intelligent ship detection plays a very important role in the marine traffic safety and the port management. Current detection methods mainly focus on synthetic aperture radar (SAR) images, which is of great significance to the field of ship detection. However, these methods sometimes cannot meet the real-time requirement. To solve the problems, a novel ship detection network based on SSD (Single Shot Detector), named NSD-SSD, is proposed in this paper. Nowadays, the surveillance system is widely used in the indoor and outdoor environment, and its combination with deep learning greatly promotes the development of intelligent object detection and recognition. The NSD-SSD uses visual images captured by surveillance cameras to achieve real-time detection and further improves detection performance. First, dilated convolution and multiscale feature fusion are combined to improve the small objects’ performance and detection accuracy. Second, an improved prediction module is introduced to enhance deeper feature extraction ability of the model, and the mean Average Precision (mAP) and recall are significant improved. Finally, the prior boxes are reconstructed by using the K-means clustering algorithm, the Intersection-over-Union (IoU) is higher, and the visual effect is better. The experimental results based on ship images show that the mAP and recall can reach 89.3% and 93.6%, respectively, which outperforms the representative model (Faster R-CNN, SSD, and YOLOv3). Moreover, our model’s FPS is 45, which can meet real-time detection acquirement well. Hence, the proposed method has the better overall performance and achieves higher detection efficiency and better robustness.

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“…Traditional ship detection mostly relies on artificially designed features and is easily affected by environmental factors. With the development of deep learning, various deep models have been developed for ship target detection, for example, Zou et al [7] proposed a ship target detection method based on improved SSD model, Wang et al [8] proposed a ship target detection method based on Yolov3. However, these models just use the information of bounding box for object detection.…”

Section: Introductionmentioning

confidence: 99%

Ship target detection based on multitask learning

Junran

Zhang²,

Liu³

et al. 2022

Fourteenth International Conference on Digital Image Processing (ICDIP 2022)

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Ship target detection is of great significance in marine surveillance, rescue and so on. In this paper, in order to improve the performance of ship target detection, we proposed a ship target detection method based on multi task learning. There are mainly two contributions. Firstly, we designed a multi-task learning model by integrating segmentation module to the faster RCNN model. Through the strategies of feature sharing and joint learning, it is helpful to improve the accuracy of target detection with the assistance of segmentation; Secondly, in order to deal with the impact of initial anchor frame scale on target detection accuracy, we introduced an adaptive anchor width height ratio setting method based on improved K-means algorithm, by adaptively select initial anchor size suitable for the characteristics of ship targets, it is beneficial to further improve the detection accuracy. Moreover, we constructed an extended version of ship image data set including 14614 images belonging to 13 categories. Experimental results demonstrated that the proposed model can effectively improve the accuracy of ship target detection; and the comparison and the ablation experiments further validated the strategies of multi-task joint learning and adaptive anchor size setting is helpful for improving the performance of ship target detection.

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Ship target detection and identification based on SSD_MobilenetV2

Cited by 40 publications

References 10 publications

A Study on Small-Scale Ship Detection Based on Attention Mechanism

A Study on Small-Scale Ship Detection Based on Attention Mechanism

NSD-SSD: A Novel Real-Time Ship Detector Based on Convolutional Neural Network in Surveillance Video

Ship target detection based on multitask learning

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