Detecting small objects on highways is a novel research topic.Due to the small pixel of objects on highways,traditional detectors have difficulty in capturing discriminative features.Additionally,the imbalance of feat...Detecting small objects on highways is a novel research topic.Due to the small pixel of objects on highways,traditional detectors have difficulty in capturing discriminative features.Additionally,the imbalance of feature fusion methods and the inconsistency between classification and regression tasks lead to poor detection performance on highways.In this paper,we propose a balance feature fusion and task-specific encoding network to address these issues.Specifically,we design a balance feature pyramid network(FPN)to integrate the importance of each layer of feature maps and construct long-range dependencies among them,thereby making the features more discriminative.In addition,we present task-specific decoupled head,which utilizes task-specific encoding to moderate the imbalance between the classification and regression tasks.As demonstrated by extensive experiments and visualizations,our method obtains outstanding detection performance on small object detection on highways(HSOD)dataset and AI-TOD dataset.展开更多
基金supported by the National Natural Science Foundation of China(Nos.61906168 and 62202337)the Zhejiang Provincial Natural Science Foundation of China(Nos.LY23F020023 and LZ23F020001)+1 种基金the Construction of Hubei Provincial Key Laboratory for Intelligent Visual Monitoring of Hydropower Projects(No.2022SDSJ01)the Hangzhou AI Major Scientific and Technological Innovation Project(No.2022AIZD0061)。
文摘Detecting small objects on highways is a novel research topic.Due to the small pixel of objects on highways,traditional detectors have difficulty in capturing discriminative features.Additionally,the imbalance of feature fusion methods and the inconsistency between classification and regression tasks lead to poor detection performance on highways.In this paper,we propose a balance feature fusion and task-specific encoding network to address these issues.Specifically,we design a balance feature pyramid network(FPN)to integrate the importance of each layer of feature maps and construct long-range dependencies among them,thereby making the features more discriminative.In addition,we present task-specific decoupled head,which utilizes task-specific encoding to moderate the imbalance between the classification and regression tasks.As demonstrated by extensive experiments and visualizations,our method obtains outstanding detection performance on small object detection on highways(HSOD)dataset and AI-TOD dataset.
