Ship detection based on the multi-scale visual saliency model

Hao-guang, 赵浩光 Zhao; Ping, 王平 Wang; Chao, 董超 Dong; Yang, 尚洋 Shang

doi:10.3788/ope.20202806.1395

Cited by 2 publications

(4 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It was verified that the robustness was good, as shown in Figure 14. Therefore, this model can be transferred to target detection for aerial imagery [28][29][30]. The detection effect of incomplete targets has always been a problem in the target detection field because only the local part of the target can be shown, so it is difficult for the network to collect the target-complete features.…”

Section: Robustness Testingmentioning

confidence: 99%

See 1 more Smart Citation

Research on the Automatic Detection of Ship Targets Based on an Improved YOLO v5 Algorithm and Model Optimization

Sun,

Wu,

et al. 2024

Mathematics

View full text Add to dashboard Cite

Because of the vast ocean area and the large amount of high-resolution image data, ship detection and data processing have become more difficult. These difficulties can be solved using the artificial intelligence interpretation method. The efficient and accurate detection ability of ship target detection has been widely recognized with the increasing application of deep learning technology. It is widely used in the practice of ship target detection. Firstly, we set up a data set concerning ship targets by collecting and training a large number of images. Then, we improved the YOLO v5 algorithm. The feature specify module (FSM) is used in the improved algorithm. The improved YOLO v5 algorithm was applied to ship detection practice under the framework of Anaconda. Finally, the training results were optimized, and the false alarm rate was reduced. The detection rate was improved. According to the statistics pertaining to experimental results with other algorithm models, the improved YOLO v5 algorithm can effectively suppress conflicting information, and the detection ability of ship details is improved. This work has accumulated valuable experience for related follow-up research.

show abstract

Section: Robustness Testingmentioning

confidence: 99%

“…It was verified that the robustness was good, as shown in Figure 14. Therefore, this model can be transferred to target detection for aerial imagery [28][29][30].…”

Section: Robustness Testingmentioning

confidence: 99%

Research on the Automatic Detection of Ship Targets Based on an Improved YOLO v5 Algorithm and Model Optimization

Sun,

Wu,

et al. 2024

Mathematics

View full text Add to dashboard Cite

show abstract

“…To overcome this challenge, a Balance Gaussian-weighted BG-NMS algorithm is introduced. The BG-NMS can be expressed as Equation (9).…”

Section: Proposing Bg-nms To Replace Nmsmentioning

confidence: 99%

“…Its Figure of Merit (FoM) is 25% higher than the CFAR algorithm. Zhao et al [9] proposed a ship detection method combining multi-scale visual saliency, capable of successfully detecting ships of different sizes and orientations while overcoming interference from complex backgrounds. This method achieved a detection rate of 93% and a false alarm rate of 4%.…”

mentioning

confidence: 99%

Ship Target Detection in Optical Remote Sensing Images Based on E2YOLOX-VFL

Zhao,

Wu,

Yuan

2024

Remote Sensing

View full text Add to dashboard Cite

In this research, E2YOLOX-VFL is proposed as a novel approach to address the challenges of optical image multi-scale ship detection and recognition in complex maritime and land backgrounds. Firstly, the typical anchor-free network YOLOX is utilized as the baseline network for ship detection. Secondly, the Efficient Channel Attention module is incorporated into the YOLOX Backbone network to enhance the model’s capability to extract information from objects of different scales, such as large, medium, and small, thus improving ship detection performance in complex backgrounds. Thirdly, we propose the Efficient Force-IoU (EFIoU) Loss function as a replacement for the Intersection over Union (IoU) Loss, addressing the issue whereby IoU Loss only considers the intersection and union between the ground truth boxes and the predicted boxes, without taking into account the size and position of targets. This also considers the disadvantageous effects of low-quality samples, resulting in inaccuracies in measuring target similarity, and improves the regression performance of the algorithm. Fourthly, the confidence loss function is improved. Specifically, Varifocal Loss is employed instead of CE Loss, effectively handling the positive and negative sample imbalance, challenging samples, and class imbalance, enhancing the overall detection performance of the model. Then, we propose Balanced Gaussian NMS (BG-NMS) to solve the problem of missed detection caused by the occlusion of dense targets. Finally, the E2YOLOX-VFL algorithm is tested on the HRSC2016 dataset, achieving a 9.28% improvement in mAP compared to the baseline YOLOX algorithm. Moreover, the detection performance using BG-NMS is also analyzed, and the experimental results validate the effectiveness of the E2YOLOX-VFL algorithm.

show abstract

Ship detection based on the multi-scale visual saliency model

Cited by 2 publications

References 0 publications

Research on the Automatic Detection of Ship Targets Based on an Improved YOLO v5 Algorithm and Model Optimization

Research on the Automatic Detection of Ship Targets Based on an Improved YOLO v5 Algorithm and Model Optimization

Ship Target Detection in Optical Remote Sensing Images Based on E2YOLOX-VFL

Contact Info

Product

Resources

About