Improved detector in orchard via top-to-down texture enhancement and adaptive region-aware feature fusion

Wei Sun, Yulong Tian, Qianzhou Wang, Jin Lu, Xianguang Kong, Yanning Zhang

科研成果: 期刊稿件文章同行评审

摘要

Accurate target detection in complex orchard environments is the basis for automatic picking and pollination. The characteristics of small, clustered and complex interference greatly increase the difficulty of detection. Toward this end, we explore a detector in the orchard and improve the detection ability of complex targets. Our model includes two core designs to make it suitable for reducing the risk of error detection due to small and camouflaged object features. Multi-scale texture enhancement design focuses on extracting and enhancing more distinguishable features for each level with multiple parallel branches. Our adaptive region-aware feature fusion module extracts the dependencies between locations and channels, potential cross-relations among different levels and multi-types information to build distinctive representations. By combining enhancement and fusion, experiments on various real-world datasets show that the proposed network can outperform previous state-of-the-art methods, especially for detection in complex conditions.

源语言英语
页(从-至)2811-2823
页数13
期刊Complex and Intelligent Systems
10
2
DOI
出版状态已出版 - 4月 2024

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