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Logo detection has been widely used in intellectual property protection and product brand management in recent years. Aiming at the complex background and multi-scale problems in Logo detection, a Logo detection algorithm based on improved Mosaic data enhancement and feature fusion was proposed. Six original images were randomly flipped, scaled and combined to form a composite image, which was used as the training input of YOLOv4 model together with single image and a composite of four original images, and the optimal proportion of the three input was determined. Meanwhile, a new training strategy was used. The improved Mosaic data enhancement further enriched the scale and context of Logo objects, enabling the model to learn the global and local features better. Based on the path integration network (PANet), some operations such as cross-layer connection, repeated stacking, direct connection and weighted feature fusion were introduced. The improved PANet enlarged the receptive field of the model and enhanced the multi-scale feature expression ability of the model. Experimental results show that the proposed MP-YOLOv4 algorithm can reduce the model size by 21.7% and reach 67.4% average precision when IoU(Intersection of Union) equals 0.5, which is 2.4% higher than YOLOv4. At the same time, the detection performance of multi-scale targets is improved.