2026-06-27T15:22:54Zhttps://docta.ucm.es/rest/oai/request
oai:docta.ucm.es:20.500.14352/720872023-07-19T05:25:41Zcom_20.500.14352_14col_20.500.14352_15
Docta Complutense author Cheng, Jianghao author Liu, Yanyan author Li, Guoning author Li, Jin author Peng, Jiantao author Hong, Jintao 2023-06-22T11:05:07Z 2023-06-22T11:05:07Z 2022-05-26 2076-3417 10.3390/app12115402 https://hdl.handle.net/20.500.14352/72087 https://doi.org/10.3390/app12115402 https://www.mdpi.com/2076-3417/12/11/5402/htm To solve the problem that small drones in the sky are easily confused with background objects and difficult to detect, according to the characteristics of irregular movement, small size, and changeable shape of drones, using a regional target recognition algorithm, the structure characteristics of Group Convolution (GC) in Resnext50 are absorbed. The optimized GC-faster-RCNN is obtained by improving the Fast-RCNN algorithm and the following methods are performed. First, a clustering method is used to analyze the dataset, and appropriate prior bounding box types are obtained. Second, the Resnext50 is used to replace the original feature extraction network, and the improved channel attention mechanism is integrated into its network output to enhance its feature map information. Then, we calculate its effective receptive field according to the Feature Pyramid Network (FPN) structure and redesign the prior bounding box of the corresponding size to construct a multi-scale detection network for small targets. Experiments show that the algorithm has a recognition accuracy of up to 94.8% under 1080 P image quality, and a recognition speed of 8FPS, which can effectively detect the positions of 1–5 small UAVs in a picture. This method provides an effective positioning detection for low-altitude UAVs. eng Atribución 3.0 España An Efficient Detection Approach for Unmanned Aerial Vehicle (UAV) Small Targets Based on Group Convolution journal article URL https://docta.ucm.es/bitstreams/4c6ebe08-788d-4f39-b08e-3b29e3174336/download File MD5 feef9f5a1030f99d93d5e932c2ae71b0 3640241 application/pdf applsci-12-05402.pdf URL https://docta.ucm.es/bitstreams/b6dee4b1-8268-4002-b393-78567ec5abaf/download File MD5 b602b333d0c2cf6a07cc8e4975c36ca3 55667 text/plain applsci-12-05402.pdf.txt