This paper presents a practical and lightweight solution for enhancing child detection in low-quality surveillance footage, a critical component in real-world missing child alert and daycare monitoring systems. Building upon the efficient YOLOv11n architecture, we propose a deployment-ready pipeline that improves detection under challenging conditions including occlusion, small object size, low resolution, motion blur, and poor lighting commonly found in existing CCTV infrastructures. Our approach introduces a domain-specific augmentation strategy that synthesizes realistic child placements using spatial perturbations such as partial visibility, truncation, and overlaps, combined with photometric degradations including lighting variation and noise. To improve recall of small and partially occluded instances, we integrate Slicing Aided Hyper Inference (SAHI) at inference time. All components are trained and evaluated on a filtered, child-only subset of the Roboflow Daycare dataset. Compared to the baseline YOLOv11n, our enhanced system achieves a mean Average Precision at 0.5 IoU (mAP@0.5) of 0.967 and a mean Average Precision averaged over IoU thresholds from 0.5 to 0.95 (mAP@0.5:0.95) of 0.783, yielding absolute improvements of 0.7 percent and 2.3 percent, respectively, without architectural changes. Importantly, the entire pipeline maintains compatibility with low-power edge devices and supports real-time performance, making it particularly well suited for low-cost or resource-constrained industrial surveillance deployments. The example augmented dataset and the source code used to generate it are available at: https://github.com/html-ptit/Data-Augmentation-YOLOv11n-child-detection

翻译：本文提出了一种实用且轻量化的解决方案，用于增强低质量监控视频中的儿童检测能力，这是现实世界中失踪儿童警报和日托监控系统的关键组成部分。基于高效的YOLOv11n架构，我们提出了一个可直接部署的流程，旨在改善在现有闭路电视基础设施中常见的遮挡、小目标尺寸、低分辨率、运动模糊和光照不足等挑战性条件下的检测性能。我们的方法引入了一种领域特定的数据增强策略，通过空间扰动（如部分可见性、截断和重叠）结合光度退化（包括光照变化和噪声）来合成逼真的儿童放置场景。为提高对小目标和部分遮挡实例的召回率，我们在推理时集成了切片辅助超推理（SAHI）技术。所有组件均在Roboflow日托数据集的过滤后纯儿童子集上进行训练和评估。与基线YOLOv11n相比，我们的增强系统在0.5交并比下的平均精度（mAP@0.5）达到0.967，在0.5至0.95交并比阈值范围内的平均精度（mAP@0.5:0.95）达到0.783，分别实现了0.7个百分点和2.3个百分点的绝对提升，且无需改变网络架构。重要的是，整个流程保持与低功耗边缘设备的兼容性，并支持实时性能，使其特别适用于低成本或资源受限的工业监控部署。示例增强数据集及生成所用的源代码发布于：https://github.com/html-ptit/Data-Augmentation-YOLOv11n-child-detection