Improved Aircraft Detection of Optical Remote Sensing Image Based on Faster R-CNN

To address the issues of extremely large image size, small target detection, and complex background interference in aircraft detection task of optical remote sensing image, an improved Faster R-CNN aircraft detection algorithm based on the fusion of lightweight feature extraction network and attention mechanism is proposed. The proposed algorithm's ability to detect small targets is significantly enhanced by removing the deep feature layer of small target detection redundancy, which also results in a 38. 4% reduction in the number of network parameters, enabling lightweight processing and significantly improving the reasoning speed. To strengthen the feature extraction ability and weaken the background interference, convolutional block attention module is creatively presented only in the backbone of the feature extraction network, which successfully increases the detection ability of the model to aircraft targets. To avoid repeated reasoning and inconsistent prediction results, the midline single frame prediction post-processing mode is used in the test reasoning stage to predict the aircraft target in the overlapping area in a single frame. The experiment demonstrates that the improved algorithm achieves a final mF1 score of 88. 97, which is 3. 5% higher than the original algorithm on the optical remote sensing dataset.