To address the problem of detail feature loss of low-altitude small objects during multi-layer down-sampling, a small object detection model ES-YOLO is proposed, based on detail feature enhancement and redundant feature suppression. The method is built upon the lightweight YOLOv5s framework and constructs a dual-feature optimization mechanism consisting of spatial detail enhancement (SDE) and redundant feature suppression (RFS) modules. SDE collaborates dynamic upsampling with transposed convolution upsampling to achieve scale-adaptive fine recovery of spatial details and structural consistency reconstruction, enhancing small object texture and boundary information. RFS models feature dependencies across both channel and spatial dimensions to suppress background noise and redundant responses, improving feature purity and object saliency. Experimental results show that ES-YOLO achieves improvements of 12.97 percentage point and 9.22 percentage point in mAP@0.5and mAP@[0.5:0.95], respectively, compared to YOLOv5s on the VisDrone2019 dataset. The proposed model requires only 38.59% of the GFLOPs of YOLOv8m, achieving a significant reduction in computational cost.