Abstract
In order to enhance the recognition rate of small object in UAV reconnaissance images, this paper proposes a detection algorithm based on MobileNet-SSD, soft-NMS and haar-like features cascade classifier. First, input the batch reconnaissance images into the improved SSD detection network based on MobileNet v1 to obtain the initial detection frame; then use the soft-NMS algorithm to remove redundant duplicate frames to improve the detection accuracy; finally, all the detected images obtained after non-maximum suppression are input into the Haar-cascade classifier in batches, and are jointly verified with the detection results of the SSD model to further boost the recognition accuracy.
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Qian, Y., Wu, G., Sun, H., Li, W., Xu, Y. (2021). Research on Small Object Detection in UAV Reconnaissance Images Based on Haar-Like Features and MobileNet-SSD Algorithm. In: Xu, Z., Parizi, R.M., Loyola-González, O., Zhang, X. (eds) Cyber Security Intelligence and Analytics. CSIA 2021. Advances in Intelligent Systems and Computing, vol 1342. Springer, Cham. https://doi.org/10.1007/978-3-030-70042-3_101
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DOI: https://doi.org/10.1007/978-3-030-70042-3_101
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