Underwater camouflage objects detection method for unmanned surface vessels
The environmental perception capability of unmanned surface vessels(USVs)is limited by factors such as the complexity of the background,diverse shapes,and camouflage of the targets to be detected.Conventional methods struggle to accurately detect and evaluate underwater camouflage objects in such scenarios.To address the diversification and complexity of detection scenarios,this paper proposes a lightweight camouflage objects detection method for unmanned surface vessels,called MFLNet(Multi-Feature Learning Network),based on a multi-task learning strategy.It enhances the USV's ability to detect underwater camouflage objects by leveraging the image gradient perception task.Initially,the feature extraction task is decoupled into semantic feature extraction and gradient feature extraction.Then,image gradient features are introduced into high-level semantic features,and initial prediction maps are generated through the proposed Multi-Scale Context Attention module.Finally,accurate final predictions are generated through feature corrections at each layer.Experi-mental results show that MFLNet achieves Sα values of 0.824 and 0.851 on the CAMO-Test and NC4K-Test datasets,re-spectively.Compared to models with the same strategy but reduced parameter counts by 65％,MFLNet achieves a detection speed of 73.7 frames per second,meeting the real-time data transmission requirements for underwater detection and demon-strating practical application value.
万方数据
维普
