Aiming at the problem of insufficient detection accuracy of complex beach small object tourists， a beach small object tourist detection method based on multi-layer feature map information fusion was proposed， and the context and feature map information was used to improve the detection rate of small object tourists. Firstly， GCSAM structure was proposed by combining the more comprehensive and effective GAM attention mechanism idea with CSP structure. The GCSAM structure was used to enhance the cross-latitude receptive field of YOLOv5 backbone. The backbone network was focused on small object feature learning. Secondly， the PANet structure was replaced by BIFPN structure in YOLOv5 network to complete transmission of feature information across layers at the neck network， more context was included in the feature map. Finally， the power transform was used to improve CIOU_Loss to Alpha-CIOU_Loss in YOLOv5 network， and the regression accuracy of prediction frame was effectively improved. Experimental results showed that in comparison with original YOLOv5 network， the precision was improved by 2.00%， the recall was improved by 5.33%， and the mean average precision was improved by 4.36% with real-time requirements. Moreover， this method had better robustness in the case of dense tourists， occlusion， and smaller objects.