Multi-granularity feature enhancement network for maritime ship detection  

作　　者：Li Ying Duoqian Miao Zhifei Zhang Hongyun Zhang Witold Pedrycz 

机构地区：[1]Department of Computer Science and Technology,Tongji University,Shanghai,China [2]Project Management Office of China National Scientific Seafloor Observatory,Tongji University,Shanghai,China [3]Department of Electrical and Computer Engineering,Alberta,Edmonton,Canada [4]System Research Institute,Polish Academy of Sciences,Warsaw,Poland

出　　处：《CAAI Transactions on Intelligence Technology》2024年第3期649-664,共16页智能技术学报（英文）

基　　金：National Key Research and Development Program of China,Grant/Award Number:2022YFB3104700;National Natural Science Foundation of China,Grant/Award Numbers:62376198,61906137,62076040,62076182,62163016,62006172;The China National Scientific Sea‐floor Observatory,The Natural Science Foundation of Shanghai,Grant/Award Number:22ZR1466700;The Jiangxi Provincial Natural Science Fund,Grant/Award Number:20212ACB202001。

摘　　要：Due to the characteristics of high resolution and rich texture information,visible light images are widely used for maritime ship detection.However,these images are suscep-tible to sea fog and ships of different sizes,which can result in missed detections and false alarms,ultimately resulting in lower detection accuracy.To address these issues,a novel multi-granularity feature enhancement network,MFENet,which includes a three-way dehazing module(3WDM)and a multi-granularity feature enhancement module(MFEM)is proposed.The 3WDM eliminates sea fog interference by using an image clarity automatic classification algorithm based on three-way decisions and FFA-Net to obtain clear image samples.Additionally,the MFEM improves the accuracy of detecting ships of different sizes by utilising an improved super-resolution reconstruction con-volutional neural network to enhance the resolution and semantic representation capa-bility of the feature maps from YOLOv7.Experimental results demonstrate that MFENet surpasses the other 15 competing models in terms of the mean Average Pre-cision metric on two benchmark datasets,achieving 96.28%on the McShips dataset and 97.71%on the SeaShips dataset.

关 键 词：object classification object recognition rough sets rough set theory 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]