Few-Shot Air Object Detection Network

• Published in: Electronics (Basel) Vol. 12; no. 19; p. 4133
• Main Authors: Cai, Wei, Wang, Xin, Jiang, Xinhao, Yang, Zhiyong, Di, Xingyu, Gao, Weijie
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.10.2023
• Subjects: Algorithms; Computer networks; Deep learning; Feature extraction; Image processing; Localization; Machine learning; Mathematical optimization; Neural networks; Object recognition; Object recognition (Computers); Pattern recognition; Sensors; China
• ISSN: 2079-9292; 2079-9292
• DOI: 10.3390/electronics12194133

Focusing on the problem of low detection precision caused by the few-shot and multi-scale characteristics of air objects, we propose a few-shot air object detection network (FADNet). We first use a transformer as the backbone network of the model and then build a multi-scale attention mechanism (MAM) to deeply fuse the W- and H-dimension features extracted from the channel dimension and the local and global features extracted from the spatial dimension with the object features to improve the network’s performance when detecting air objects. Second, the neck network is innovated based on the path aggregation network (PANet), resulting in an improved path aggregation network (IPANet). Our proposed network reduces the information lost during feature transfer by introducing a jump connection, utilizes sparse connection convolution, strengthens feature extraction abilities at all scales, and improves the discriminative properties of air object features at all scales. Finally, we propose a multi-scale regional proposal network (MRPN) that can establish multiple RPNs based on the scale types of the output features, utilizing adaptive convolutions to effectively extract object features at each scale and enhancing the ability to process multi-scale information. The experimental results showed that our proposed method exhibits good performance and generalization, especially in the 1-, 2-, 3-, 5-, and 10-shot experiments, with average accuracies of 33.2%, 36.8%, 43.3%, 47.2%, and 60.4%, respectively. The FADNet solves the problems posed by the few-shot characteristics and multi-scale characteristics of air objects, as well as improving the detection capabilities of the air object detection model.