Abstract: To address the challenges of small object size, feature degradation, strong background interference, and the difficulty of balancing detection accuracy with lightweight deployment in UAV aerial imagery, this paper proposes BEL-YOLO, a lightweight small-object detection model with coordinated optimization based on YOLO11s. The model is specifically optimized in terms of multi-scale feature fusion, upsampling detail reconstruction, high-resolution prediction, and bounding-box regression. First, a Bidirectional Feature Pyramid Network (BiFPN) is introduced into the neck to enhance the interaction between shallow spatial details and deep semantic information through bidirectional weighted cross-scale feature fusion, thereby improving multi-scale feature representation for small objects. Second, a Gated Shift Channel Efficient Up-Convolution Block (GSC-EUCB) is proposed, which integrates channel rearrangement, shift-channel mixing, and residual gated fusion to strengthen cross-channel interaction and local detail reconstruction while alleviating weak-feature loss during upsampling. Subsequently, Detect_LSCD-P2 is designed based on the LSCD shared-convolution mechanism. The detection scales are reconstructed from P3/P4/P5 to P2/P3/P4, allowing high-resolution features to enhance tiny-object perception while cross-scale parameter sharing reduces redundancy in prediction branches. Finally, a Normalized Wasserstein Inner-Shape Loss (NWIS-Loss) is proposed by jointly modeling the distribution-distance constraint of NWD and the internal-overlap and shape constraints of Inner-ShapeIoU, thereby improving robustness to positional deviations of tiny bounding boxes and enhancing geometric consistency. Experimental results on the VisDrone2019 dataset show that, compared with YOLO11s, BEL-YOLO reduces the number of parameters by 29.6%, while improving mAP50 and mAP50–95 by 8.3 and 5.6 percentage points, respectively, and increasing precision and recall by 6.2 and 7.2 percentage points, respectively, while maintaining an inference speed of 157.41 FPS. Further experiments on the TinyPerson dataset show improvements of 5.3 and 1.84 percentage points in mAP50 and mAP50–95, respectively, demonstrating good generalization to extremely small objects and complex backgrounds. Overall, BEL-YOLO effectively improves small-object detection accuracy while controlling model size and inference overhead, providing an efficient solution for real-time small-object detection and edge deployment on resource-constrained UAV platforms.

摘要： 针对无人机航拍场景中小目标尺度小、特征易衰减、复杂背景干扰强，以及检测精度与轻量化部署难以兼顾的问题，本文基于YOLO11s提出一种轻量化协同优化小目标检测模型BEL-YOLO。模型围绕多尺度特征融合、上采样细节重建、高分辨率预测和边界框回归优化进行针对性设计。首先，在颈部网络中引入双向特征金字塔网络（BiFPN），通过双向加权跨尺度融合增强浅层空间细节与深层语义信息交互，提高小目标多尺度特征表征能力。其次，提出门控通道移位增强上采样模块（GSC-EUCB），利用通道重排、通道移位混合与残差门控融合增强跨通道信息交互和局部细节恢复，降低上采样过程中的弱小目标特征损失。随后，基于LSCD共享卷积机制设计Detect_LSCD-P2，将检测尺度由P3/P4/P5重构为P2/P3/P4，利用高分辨率特征强化微小目标感知，并通过跨尺度参数共享减少预测分支参数冗余。最后，提出归一化Wasserstein内部形状联合损失函数（NWIS-Loss），将NWD的分布距离约束与Inner-ShapeIoU的内部重叠及形状约束进行联合建模，提高微小边界框对位置偏移的鲁棒性与边界几何一致性。在VisDrone2019数据集上的实验结果表明，与YOLO11s相比，BEL-YOLO的参数量减少29.6%，mAP50和mAP50-95分别提升8.3和5.6个百分点，Precision和Recall分别提升6.2和7.2个百分点，同时保持157.41 FPS的推理速度。TinyPerson数据集上的实验进一步表明，BEL-YOLO的mAP50和mAP50-95较YOLO11s分别提升5.3和1.84个百分点，验证了模型在极小目标及复杂背景场景下的良好泛化能力。综合结果表明，BEL-YOLO在提升航拍小目标检测精度的同时，有效控制了模型规模与推理开销，为资源受限无人机平台的小目标实时检测与边缘部署提供了有效方案。