MULTI-SCALE REMOTE SENSING IMAGE TARGET DETECTION METHOD BASED ON OPTIMIZED FEATURE FUSION

A DAFFNet remote sensing image object detection algorithm is proposed to solve the problem of low accuracy of multi-scale object detection in the remote sensing image scene. Based on SSD, the algorithm was improved in three aspects. We designed a group-based feature fusion method to enhance the ability to acquire multi-scale feature information. A multi-dimensional feature optimized method based on the attention mechanism was introduced to solve the difficulty of target classification in a complex background. The focal loss was used as a new bounding box confidential loss function to make the model focus on the positive samples that were difficult to classify, so as to improve the interference caused by the imbalance of positive and negative samples to target classification. The model was evaluated on the remote sensing public dataset NWPU VHR-10. The experimental result shows that the proposed algorithm improves the mean average precision by 5.1 percentage points compared with the original algorithm, which can effectively increase the object detection accuracy of remote sensing image.