Ship detection based on semantic aggregation for video surveillance images with complex backgrounds
Background Ship detection in video surveillance images holds significant practical value. However, the background in these images is often complex, complicating the achievement of an optimal balance between detection precision and speed. Method This study proposes a ship detection method that levera...
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| Format: | Article |
| Language: | English |
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PeerJ Inc.
2024-12-01
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| Series: | PeerJ Computer Science |
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| Online Access: | https://peerj.com/articles/cs-2624.pdf |
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| _version_ | 1846109163779784704 |
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| author | Yongmei Ren Haibo Liu Jie Yang Xiaohu Wang Wei He Dongrui Xiao |
| author_facet | Yongmei Ren Haibo Liu Jie Yang Xiaohu Wang Wei He Dongrui Xiao |
| author_sort | Yongmei Ren |
| collection | DOAJ |
| description | Background Ship detection in video surveillance images holds significant practical value. However, the background in these images is often complex, complicating the achievement of an optimal balance between detection precision and speed. Method This study proposes a ship detection method that leverages semantic aggregation in complex backgrounds. Initially, a semantic aggregation module merges deep features, rich in semantic information, with shallow features abundant in location details, extracted via the front-end network. Concurrently, these shallow features are reshaped through the reorg layer to extract richer feature information, and then these reshaped shallow features are integrated with deep features within the feature fusion module, thereby enhancing the capability for feature fusion and improving classification and positioning capability. Subsequently, a multiscale object detection layer is implemented to enhance feature expression and effectively identify ship objects across various scales. Moreover, the distance intersection over union (DIoU) metric is utilized to refine the loss function, enhancing the detection precision for ship objects. Results The experimental results on the SeaShips dataset and SeaShips_enlarge dataset demonstrate that the mean average precision@0.5 (mAP@0.5) of this proposed method reaches 89.30% and 89.10%, respectively. Conclusions The proposed method surpasses other existing ship detection techniques in terms of detection effect and meets real-time detection requirements, underscoring its engineering relevance. |
| format | Article |
| id | doaj-art-aa09537c3aca4e9f81b02e417346938d |
| institution | Kabale University |
| issn | 2376-5992 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj-art-aa09537c3aca4e9f81b02e417346938d2024-12-25T15:05:40ZengPeerJ Inc.PeerJ Computer Science2376-59922024-12-0110e262410.7717/peerj-cs.2624Ship detection based on semantic aggregation for video surveillance images with complex backgroundsYongmei Ren0Haibo Liu1Jie Yang2Xiaohu Wang3Wei He4Dongrui Xiao5School of Electrical and Information Engineering, Hunan Institute of Technology, Hengyang, ChinaSchool of Electrical and Information Engineering, Hunan Institute of Technology, Hengyang, ChinaSchool of Information Engineering, Wuhan University of Technology, Wuhan, ChinaCollege of Intelligent Manufacturing and Mechanical Engineering, Hunan Institute of Technology, Hengyang, ChinaSchool of Electrical and Information Engineering, Hunan Institute of Technology, Hengyang, ChinaSchool of Electrical and Information Engineering, Hunan Institute of Technology, Hengyang, ChinaBackground Ship detection in video surveillance images holds significant practical value. However, the background in these images is often complex, complicating the achievement of an optimal balance between detection precision and speed. Method This study proposes a ship detection method that leverages semantic aggregation in complex backgrounds. Initially, a semantic aggregation module merges deep features, rich in semantic information, with shallow features abundant in location details, extracted via the front-end network. Concurrently, these shallow features are reshaped through the reorg layer to extract richer feature information, and then these reshaped shallow features are integrated with deep features within the feature fusion module, thereby enhancing the capability for feature fusion and improving classification and positioning capability. Subsequently, a multiscale object detection layer is implemented to enhance feature expression and effectively identify ship objects across various scales. Moreover, the distance intersection over union (DIoU) metric is utilized to refine the loss function, enhancing the detection precision for ship objects. Results The experimental results on the SeaShips dataset and SeaShips_enlarge dataset demonstrate that the mean average precision@0.5 (mAP@0.5) of this proposed method reaches 89.30% and 89.10%, respectively. Conclusions The proposed method surpasses other existing ship detection techniques in terms of detection effect and meets real-time detection requirements, underscoring its engineering relevance.https://peerj.com/articles/cs-2624.pdfImage processingShip detectionSemantic aggregationFeature fusionVideo surveillance images |
| spellingShingle | Yongmei Ren Haibo Liu Jie Yang Xiaohu Wang Wei He Dongrui Xiao Ship detection based on semantic aggregation for video surveillance images with complex backgrounds PeerJ Computer Science Image processing Ship detection Semantic aggregation Feature fusion Video surveillance images |
| title | Ship detection based on semantic aggregation for video surveillance images with complex backgrounds |
| title_full | Ship detection based on semantic aggregation for video surveillance images with complex backgrounds |
| title_fullStr | Ship detection based on semantic aggregation for video surveillance images with complex backgrounds |
| title_full_unstemmed | Ship detection based on semantic aggregation for video surveillance images with complex backgrounds |
| title_short | Ship detection based on semantic aggregation for video surveillance images with complex backgrounds |
| title_sort | ship detection based on semantic aggregation for video surveillance images with complex backgrounds |
| topic | Image processing Ship detection Semantic aggregation Feature fusion Video surveillance images |
| url | https://peerj.com/articles/cs-2624.pdf |
| work_keys_str_mv | AT yongmeiren shipdetectionbasedonsemanticaggregationforvideosurveillanceimageswithcomplexbackgrounds AT haiboliu shipdetectionbasedonsemanticaggregationforvideosurveillanceimageswithcomplexbackgrounds AT jieyang shipdetectionbasedonsemanticaggregationforvideosurveillanceimageswithcomplexbackgrounds AT xiaohuwang shipdetectionbasedonsemanticaggregationforvideosurveillanceimageswithcomplexbackgrounds AT weihe shipdetectionbasedonsemanticaggregationforvideosurveillanceimageswithcomplexbackgrounds AT dongruixiao shipdetectionbasedonsemanticaggregationforvideosurveillanceimageswithcomplexbackgrounds |