{"title":"LSDNet: a lightweight ship detection network with improved YOLOv7","authors":"Cui Lang, Xiaoyan Yu, Xianwei Rong","doi":"10.1007/s11554-024-01441-9","DOIUrl":null,"url":null,"abstract":"<p>Accurate ship detection is critical for maritime transportation security. Current deep learning-based object detection algorithms have made marked progress in detection accuracy. However, these models are too heavy to be applied in mobile or embedded devices with limited resources. Thus, this paper proposes a lightweight convolutional neural network shortened as LSDNet for mobile ship detection. In the proposed model, we introduce Partial Convolution into YOLOv7-tiny to reduce its parameter and computational complexity. Meanwhile, GhostConv is introduced to further achieve lightweight structure and improve detection performance. In addition, we use Mosaic-9 data-augmentation method to enhance the robustness of the model. We compared the proposed LSDNet with other approaches on a publicly available ship dataset, SeaShips7000. The experimental results show that LSDNet achieves higher accuracy than other models with less computational cost and parameters. The test results also suggest that the proposed model can meet the requirements of real-time applications.</p>","PeriodicalId":51224,"journal":{"name":"Journal of Real-Time Image Processing","volume":"33 1","pages":""},"PeriodicalIF":2.9000,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Real-Time Image Processing","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1007/s11554-024-01441-9","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
Accurate ship detection is critical for maritime transportation security. Current deep learning-based object detection algorithms have made marked progress in detection accuracy. However, these models are too heavy to be applied in mobile or embedded devices with limited resources. Thus, this paper proposes a lightweight convolutional neural network shortened as LSDNet for mobile ship detection. In the proposed model, we introduce Partial Convolution into YOLOv7-tiny to reduce its parameter and computational complexity. Meanwhile, GhostConv is introduced to further achieve lightweight structure and improve detection performance. In addition, we use Mosaic-9 data-augmentation method to enhance the robustness of the model. We compared the proposed LSDNet with other approaches on a publicly available ship dataset, SeaShips7000. The experimental results show that LSDNet achieves higher accuracy than other models with less computational cost and parameters. The test results also suggest that the proposed model can meet the requirements of real-time applications.
期刊介绍：
Due to rapid advancements in integrated circuit technology, the rich theoretical results that have been developed by the image and video processing research community are now being increasingly applied in practical systems to solve real-world image and video processing problems. Such systems involve constraints placed not only on their size, cost, and power consumption, but also on the timeliness of the image data processed.
Examples of such systems are mobile phones, digital still/video/cell-phone cameras, portable media players, personal digital assistants, high-definition television, video surveillance systems, industrial visual inspection systems, medical imaging devices, vision-guided autonomous robots, spectral imaging systems, and many other real-time embedded systems. In these real-time systems, strict timing requirements demand that results are available within a certain interval of time as imposed by the application.
It is often the case that an image processing algorithm is developed and proven theoretically sound, presumably with a specific application in mind, but its practical applications and the detailed steps, methodology, and trade-off analysis required to achieve its real-time performance are not fully explored, leaving these critical and usually non-trivial issues for those wishing to employ the algorithm in a real-time system.
The Journal of Real-Time Image Processing is intended to bridge the gap between the theory and practice of image processing, serving the greater community of researchers, practicing engineers, and industrial professionals who deal with designing, implementing or utilizing image processing systems which must satisfy real-time design constraints.
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