As the application domains of UAVs continue to expand,unauthorized UAV flights pose serious threats to public safety.To solve the problems of false positives and negatives in detecting small intruding UAVs in complex flight scenarios,an anti-UAV target detection algorithm based on YOLOv5s-AntiUAV is proposed.Firstly,the Slim-Neck paradigm incorporating deep hyperparameter convolution is introduced to enhance feature extraction capabilities while maintaining computational efficiency.Secondly,the SPD-Conv modules are integrated into both the backbone and neck networks to improve the detection performance of small targets in low-resolution images.Finally,Alpha-CIoU is used to replace CIoU in YOLOv5s to augment the algorithms versatility.Results of comparative experiments of YOLOv5s-AntiUAV with YOLOv5s,SSD,Faster R-CNN on the Anti-UAV dataset show increases in mAP@0.5 by 1.1,12.1 and 4.9 percentage points respectively,which proves its practicality.The transfer experiments on the VisDrone2019 dataset show a 4.5 percentage points improvement in mAP@0.5 in comparison with YOLOv5s,which demonstrates that the improved algorithm is more robust than the original one.