Aimed at the disadvantages of an enhancement to the representation of deep feature map in the enhanced feature fusion network for the spatial pyramid pooling module,higher computational complexity,and difficulty in highlighting important channel features for the feature map of the detection head network in YOLOv4 algorithm,Based on this problems,a real-time object detection algorithm based on attention mechanism and multi-spatial pyramid pooling is proposed.This algorithm adopts multi-spatial pyramid pooling,extracts the local and global features,fuses multiple receptive fields,and strengths the characterization ability of the shal-low,middle and deep feature maps for the feature fusion network.The squeeze-and-excitation channel attention mechanism is intro-duced to model the relativities between channels,the weight of each channel is adaptively recalibrated to make the network pay more attention to important features.Moreover,the deep separable convolution is used to reduce the parameters of the feature fusion and detection head networks.The experimental results show that the mean average precision(mPA)of the proposed algorithm is higher than that of other 7 mainstream comparison algorithms,compared with YOLOv4,the parameters and model size are reduced by 27.85 M and 106.25 MB,respectively.The proposed algorithm not only improves the detection accuracy,but also reduces the computation-al complexity compared to the baseline algorithm,and the average speed of the algorithm reaches by 33.70 FPS,which meets the re-al-time requirement.
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