Research on Detection Technology of Autonomous Landing Based on Airborne Vision

In this paper, aiming at the cooperative target detection problem in the process of unmanned helicopter sliding down, a detection method based on complementary filtering is proposed, which fuses improved SSD algorithm and related filtering KCF algorithm. The improved deep learning SSD model redesigns the feature extraction structure to improve the detection effect of small and medium targets for the small target size and large scale change in the landing scene. Then use the detection results of the SSD model to correct the KCF detection, adjust the weight parameters, and output the final fusion detection results. The test results show that the improved model detection accuracy is significantly improved, the detection accuracy in various environments reaches 93.3%, which is higher than 86.1% of the classic SSD model and 87.5% of the Faster-rcnn model. The final proposed fusion detection algorithm has a success rate of 91.1% and a processing speed of 91 hz, which basically satisfies the requirements of the ship.