液晶与显示2026，Vol.41Issue(8)：1144-1157,14.DOI:10.3724/CJLCD.2026-0108
基于改进YOLO11n的野外小目标检测算法
Wild small object detection algorithm based on improved YOLO11n
摘要
Abstract
To address the problems of small target scale,weak texture information,complex background interference,and obvious bounding-box localization deviation in wild environments,an improved YOLO11n-based algorithm for wild small object detection is proposed.First,an adaptive sub-pixel upsampling(ASU)module is introduced to enhance feature fusion for small objects.Through sub-pixel rearrangement and attention masks,ASU strengthens the representation of edge and texture details,thereby reducing the loss of fine-grained information during upsampling.Second,a multi-scale saliency enhancement module(MultiSEM)is introduced before the detection head.By using multi-scale dilated convolutions,the module extracts spatial features under different receptive fields,and dual-pooling channel attention is further used to enhance the response of small target regions while suppressing interference from complex backgrounds.Finally,a saliency-exponential Complete Intersection over Union loss function(SE-CIoU)is constructed for bounding-box regression.By introducing a center-distance exponential penalty and occlusion-aware shape constraints,SE-CIoU strengthens the position and shape constraints between predicted boxes and ground-truth boxes,thus improving the localization accuracy and stability of small object detection.Experimental results show that,on the self-built WDD dataset,the improved algorithm achieves a precision of 88.8%,a recall of 91.4%,and an mAP@0.5 of 94.0%,which are 0.6,6.6,and 3.3 percentage points higher than those of the YOLO11n baseline network algorithm,respectively.Generalization experiments on the iWildCam2022-Small dataset show that the mAP@0.5 reaches 64.5%,which is 3.7 percentage points higher than that of YOLO11n.The results indicate that the proposed algorithm can effectively enhance detail representation,saliency response,and bounding-box localization ability for wild small objects while maintaining a lightweight detection framework.Therefore,the improved YOLO11n algorithm improves small object detection accuracy in complex wild scenes and shows certain generalization ability.关键词
野外小目标/目标检测/YOLO11n/亚像素上采样/边界框回归Key words
wild small object/object detection/YOLO11n/subpixel upsampling/bounding box regression分类
信息技术与安全科学引用本文复制引用
王岑,王彩霞,刘鹏..基于改进YOLO11n的野外小目标检测算法[J].液晶与显示,2026,41(8):1144-1157,14.基金项目
吉林省科技发展计划(No.20250102228JC)Supported by Jilin Provincial Science and Technology Development Plan(No.20250102228JC) （No.20250102228JC）
