Abstract: Due to the large amount of neural network parameters and insufficient computing power of embedded devices,it is difficult to effectively deploy neural networks on embedded platforms when using deep learning methods to identify spatial non-cooperative target components.Aiming at this problem,an improved lightweight target detection network is proposed in this paper.On the basis of YOLOv3,a new lightweight feature extraction backbone network Res2-MobileNet is designed,drawing on the ideaof Depth Separable Convolution,introducing the Bottleneck module to reduce the amount of model parameters to improve the detection speed,and introducing the Res2Net residual module to increase the sensitivity of network to small targets by increasing the modeĹs receptive field scale richness and structural depth,and combines multi-scale detection methods to recognize spatial non-cooperative target components.The experimental results show that compared with the YOLOv3 model,the size of this model is reduced by 55.5%,the detection speed is increased from 34fps to 65 fps,and the detection effect for small targets is also significantly improved.

Key words: Lightweight, Spatial non-cooperative target, Target recognition, YOLOv3