Aiming at the problem of small target (pixel ratio less than 0.02) detection that the target features are easily lost and the resolution is low, a detection method based on improved YOLOv3 (You only look once) convolutional neural network is proposed in this paper. First, the small targets in the data set are copied and transformed to enhance the network''s attention to the small targets during the training process. Second, for the scale fusion of shallow visual information and deep semantic information, a cross-scale detection layer network structure is proposed, which improves the network''s adaptability to small targets. Finally, for the detection effect of high-resolution images, a residual block transfer structure combining depth and breadth is proposed, which enriches the receptive field of deep feature maps. Experimental results show that compared with the YOLOv3 network, the precision rate of the network detection of small targets with the improved cross-scale prediction layer increased by 1.9 percentage points, and the recall rate increased by 5.9 percentage points. The precision rate of the network detection of small targets with the optimized receptive fields increased 31.6 percentage points, the recall rate increased by 46.4 percentage points.