ALFPN: Adaptive Learning Feature Pyramid Network for Small Object Detection
Haolin Chen, Qi Wang, Weijian Ruan, Jingxiang Zhu, Liang Lei, Xue Wu, Ge‐Fei Hao
Abstract
Object detection has become a crucial technology in intelligent vision systems, enabling automatic detection of target objects. While most detectors perform well on open datasets, they often struggle with small‐scale objects. This is due to the traditional top‐down feature fusion methods that weaken the semantic and location information of small objects, leading to poor classification performance. To address this issue, we propose a novel feature pyramid network, the adaptive learnable feature pyramid network (ALFPN). Our approach features an adaptive feature inspection that incorporates learnable fusion coefficients in the fusion of different levels of feature layers, aiding the network in learning features with less noise. In addition, we construct a context‐aligned supervisor that adjusts the feature maps fused at different levels to avoid scaling‐related offset effects. Our experiments demonstrate that our method achieves state‐of‐the‐art results and is highly robust for the small object detection on the TT‐100K, PASCAL VOC, and COCO datasets. These findings indicate that a model’s ability to extract discriminant features is positively correlated with its performance in detecting small objects.