Detection of weakly labeled remote sensing images based on adaptive weighted fusion

Object detection is an important computer vision task that deals with detecting instances of visual objects of a certain class in digital images. It is also the basis for many other computer vision tasks, such as instance segmentation, image caption, and object tracking. Object detection has now been widely used in many real-world applications, such as autonomous driving, robot vision, and remote sensing image detection. Especially With the development of remote sensing technology, the requirement of remote sensing image object detection technology is further improved. It is of great significance for the development of remote sensing image detection to recognize the target of remote sensing image quickly and accurately. However, supervised training of traditional object detectors requires well-labeled large-scale datasets, and for the problem of low detection accuracy on remote sensing images with dense targets and poor labeling quality, a object detection method for weakly labeled remote sensing images based on adaptive weighted fusion algorithm is proposed. Object detection experiments are done on the RSOD data set and part of the DOTA data set under different degrees of localization annotation offset, and the experimental results show that the optimization method achieves a mAP value of 73.2%, which is 10.6 percentage points higher than the original algorithm, and it significantly improves the detection accuracy of inaccurately localized and annotated targets under the maximum degree of offset of the RSOD data set.