Enhancing YOLOv11n for Reliable Child Detection in Noisy Surveillance Footage

TL;DR

This paper enhances YOLOv11n for reliable child detection in noisy, low-quality surveillance footage by introducing domain-specific augmentation and inference techniques, achieving high accuracy while maintaining real-time performance on resource-constrained devices.

Contribution

The paper proposes a novel augmentation and inference pipeline that significantly improves child detection accuracy in challenging surveillance conditions without changing the underlying architecture.

Findings

Achieved mAP@0.5 of 0.967, a 0.7% improvement over baseline.

Improved detection of small and occluded children.

Maintains real-time performance on low-power edge devices.

Abstract

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