ArticleComputational intelligence and neuroscience2022
Ship Target Detection in Optical Remote Sensing Images Based on Multiscale Feature Enhancement.
Article in Computational intelligence and neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- Apple-Net: A Model Based on Improved YOLOv5 to Detect the Apple Leaf Diseases.Plants (Basel, Switzerland) · 2022Article
- Sentiment Classification of Chinese Tourism Reviews Based on ERNIE-Gram+GCN.International journal of environmental research and public health · 2022Article
- Ship Target Detection in Optical Remote Sensing Images Based on Multiscale Feature Enhancement.Computational intelligence and neuroscience · 2022Article
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Authors and funding
6 authors.
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Abstract
Due to the multiscale characteristics of ship targets in ORSIs (optical remote sensing images), ship target detection in ORSIs based on depth learning is still facing great challenges. Aiming at the low accuracy of multiscale ship target detection in ORSIs, this paper proposes a ship target detection algorithm based on multiscale feature enhancement based on YOLO v4. Firstly, an improved mixed convolution is introduced into the IRes (inverted residual block) to form an MIRes (mixed inverted residual block). The MIRes are used to replace the Res (residual block) in the deep CSP module of the backbone network to enhance the multiscale feature extraction capability of the backbone network. Secondly, for different scale feature maps' perception fields, feature information, and the scale of the detected objects, the multiscale feature enhancement modules-SFEM (small scale feature enhancement module) and MFEM (middle scale feature enhancement module)-are proposed to enhance the feature information of the middle- and low-level feature maps, respectively, and then the enhanced feature maps are sent to the detection head for detection. Finally, experiments were implemented on the LEVIR-ship dataset and the NWPU VHR-10 dataset. The accuracy of the proposed algorithm in ship target detection reached 79.55% and 90.70%, respectively, which is improved by 3.25% and 3.56% compared with YOLO v4.
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Registered trials
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