SF-YOLO: A Novel YOLO Framework for Small Object Detection in Aerial Scenes

Abstract

Object detection models are widely applied in the fields such as video surveillance and unmanned aerial vehicles to enable the identification and monitoring of various objects on a diversity of backgrounds. The general CNN-based object detectors primarily rely on downsampling and pooling operations, often struggling with small objects that have low resolution and failing to fully leverage contextual information that can differentiate objects from complex background. To address the problems, we propose a novel YOLO framework called SF-YOLO for small object detection. Firstly, we present a spatial information perception (SIP) module to extract contextual features for different objects through the integration of space to depth operation and large selective kernel module, which dynamically adjusts receptive field of the backbone and obtains the enhanced features for richer understanding of differentiation between objects and background. Furthermore, we design a novel multi-scale feature weighted fusion strategy, which performs weighted fusion on feature maps by combining fast normalized fusion method and CARAFE operation, accurately assessing the importance of each feature and enhancing the representation of small objects. The extensive experiments conducted on VisDrone2019, Tiny-Person and PESMOD datasets demonstrate that our proposed method enables comparable detection performance to state-of-the-art detectors.