RT Journal Article
T1 An Efficient Detection Approach for Unmanned Aerial Vehicle (UAV) Small Targets Based on Group Convolution
A1 Cheng, Jianghao
A1 Liu, Yanyan
A1 Li, Guoning
A1 Li, Jin
A1 Peng, Jiantao
A1 Hong, Jintao
AB 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.
PB MPDI
SN 2076-3417
YR 2022
FD 2022-05-26
LK https://hdl.handle.net/20.500.14352/72087
UL https://hdl.handle.net/20.500.14352/72087
LA eng
NO Unión Europea. Horizonte 2020
DS Docta Complutense
RD 10 abr 2025