Remote Sensing Images Small Object Detection Algorithm With Adaptive Fusion and Hybrid Anchor Detector
A hybrid detector AEM-YOLO based on the adaptive fusion of different scale features is proposed,aiming at the problems of difficult detection of small objects in remote sensing images caused by the high background noise,dense arrangement of small objects,and wide-scale distribution.Firstly,a two-axes k-means clustering algorithm combining width and height information with scale and ratio information is proposed to generate anchor boxes with high matching degrees with remote sensing datasets.Secondly,an adaptive enhance module is designed to address information conflicts caused by direct fusion between different scale features.A lower feature layer is introduced to broadcast small object details along the bottom-up path.By using multi-task learning and scale guidance factor,the recall for objects with a high aspect ratio can be effectively improved.Finally,the experiments on the DIOR dataset show that compared with the original model,the AP of AEM-YOLO is improved by 7.8％,and increased by 5.4％,7.2％,and 8.6％in small,medium,and large object detection,respectively.
万方数据
维普
