Full Perception Head: Bridging the Gap Between Local and Global Features

Object detection is a fundamental task in computer vision that involves identifying and localizing objects within an image. Local features extracted by convolutions, etc., capture finegrained details such as edges and textures, while global features extracted by full connection layers, etc., 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 AP<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">val</inf> gains when integrated into standard object detectors. Notably, the FP-Mod-ule is a universal solution that can be seamlessly incorporated into existing detectors to boost performance. The code will be released at https://github.com/Idcogroup/FP-Head.