Aiming at small objects detection such as unmanned aerial vehicle (UAV), this paper proposes a fast object detectionalgorithm based on depthwise separable convolutions. Firstly, the inverted residuals units based on depthwise convolutionsand pointwise convolutions are used to construct a lightweight feature extraction network to improve the network’s speed.Secondly, the feature pyramid network is used to detect the five scale feature maps to improve the detection performanceof small objects. Otherwise, we make an UAV dataset based on the urban background for training and testing of ourexperiments. The experimental results show that the improved method proposed in this paper can effectively improve thedetection accuracy and real-time performance of UAVs in complex urban backgrounds, and the computation of network isgreatly reduced, thereby making it possible to achieve object detection on embedded systems.
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