Ship detection based on semantic aggregation for video surveillance images with complex backgrounds

作     者：Ren, Yongmei Liu, Haibo Yang, Jie Wang, Xiaohu He, Wei Xiao, Dongrui 

作者机构：Hunan Inst Technol Sch Elect Informat Engn Hengyang Peoples R China Wuhan Univ Technol Sch Informat Engn Wuhan Peoples R China Hunan Inst Technol Coll Intelligent Mfg & Mech Engn Hengyang Peoples R China 

出 版 物：《PEERJ COMPUTER SCIENCE》 (PeerJ Comput. Sci.)

年 卷 期：2024年第10卷

页      面：e2624-e2624页

核心收录：

基　　金：National Nature Science Foundation of China Scientific Research Fund of Hunan Provincial Education Department [24A0655, 22B0861] Hunan Provincial Natural Science Foundation of China [2022JJ50148] Undergraduate Innovation and Entrepreneurship Training of Hunan province [S202411528087, S202411528064] Guiding Planning Project of Hengyang 

主　　题：Image processing Ship detection Semantic aggregation Feature fusion Video surveillance images 

摘      要：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.