Small Object Detection of High-Resolution Images Based on Feature Fusion and Learnable Anchor

Small object detection of high-resolution images presents significant challenges. To solve the problem that downsampling and cropping of high-resolution images result in missed detections and false detections due to the loss of fine details and contextual information, an algorithm based on feature fusion and learnable anchor is proposed for small object detection of high-resolution images. Contextual and detailed features are extracted from downsampled images and cropped patches respectively, which are then fused layer-wise. The fused features are further combined with smoothed features to strengthen both fine details and contextual information. To mitigate the feature inconsistency, learnable anchor is applied to make the fused features accommodative to the location and shape of anchors. The proposed method is tested from the perspective of global inference and local inference compared to state-of-the-art detectors. The experimental results show the accuracy and effectiveness of the proposed method. © 2022 Chinese Institute of Electronics. All rights reserved.