HR-SemNet: A High-Resolution Network for Enhanced Small Object Detection With Local Contextual Semantics.

Using higher-resolution feature maps in the network is an effective approach for detecting small objects. However, high-resolution feature maps face the challenge of lacking semantic information. This has led previous methods to rely on downsampling feature maps, applying large-kernel convolution layers, and then upsampling the feature maps to obtain semantic information. However, these methods have certain limitations: first, large kernel convolutions in deeper layers typically provide significant global semantic information, but our experiments reveal that such prominent semantic information introduces background smear, which in turn leads to overfitting. Second, deep features often contain substantial redundant information, and the features of small objects are either minimal or have disappeared, which causes a degradation in detection performance when directly relying on deep features. To address these issues, we propose a high-resolution network based on local contextual semantics (HR-SemNet). The network is built on the proposed high-resolution backbone (HRB), which replaces the traditional backbone-FPN architecture by focusing all computational resources of large kernel convolutions on high-resolution feature layers to capture clearer features of small objects. Additionally, a local context semantic module (LCSM) is employed to extract semantic information from the background, confining the semantic extraction to a local window to avoid interference from large-scale backgrounds and objects. HR-SemNet decouples small object semantics from contextual semantics, with HRB and LCSM independently extracting these features. Extensive experiments and comprehensive evaluations on the VisDrone, AI-TOD, and TinyPerson datasets validate the effectiveness of the method. On the VisDrone dataset, which contains a large number of small objects, HR-SemNet improves the mean average precision (mAP) by 4.6%, reduces the computational cost (GFLOPs) by 49.9%, and decreases the parameter count by 94.9%.