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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2025  > In Press, Accepted Manuscript
J. Hua, Z. Wang, X. Tian, Q. Zou, J. Xiao, and J. Ma, “Full perception head: Bridging the gap between local and global features,” IEEE/CAA J. Autom. Sinica, 2025. doi: 10.1109/JAS.2025.125333
Citation: J. Hua, Z. Wang, X. Tian, Q. Zou, J. Xiao, and J. Ma, “Full perception head: Bridging the gap between local and global features,” IEEE/CAA J. Autom. Sinica, 2025. doi: 10.1109/JAS.2025.125333
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Full Perception Head: Bridging the Gap Between Local and Global Features

doi: 10.1109/JAS.2025.125333
Funds:  This work was supported by the National Natural Science Foundation of China (62371350, 62171324, 62471338, U1903214)
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    Abstract
  • Object detection is a fundamental task in computer vision that involves identifying and localizing objects within an image. In this context, local features capture fine-grained details such as edges and textures, while global features represent the overall structure and long-range relationships within the image. These features are crucial for accurate object detection, yet most existing methods focus on aggregating local and global features, often overlooking the importance of medium-range dependencies. To address this gap, we propose a novel Full Perception Module (FP-Module), a simple yet effective feature extraction module designed to simultaneously capture local details, medium-range dependencies, and long-range dependencies. Building on this, we construct a full perception head (FP-Head) by cascading multiple FP-Modules, enabling the prediction layer to leverage the most informative features. Experimental results in the MS COCO dataset demonstrate that our approach significantly enhances object recognition and localization, achieving 2.7−5.7 APval gains when integrated into standard object detectors. Notably, the FP-Module is a universal solution that can be seamlessly incorporated into existing detectors to boost performance. The code will be released soon.

     

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