Enhancing YOLOv11n for Reliable Child Detection in Noisy Surveillance Footage

2026-02-11

AI Summary

This work addresses the challenges of detecting children in low-quality surveillance videos—such as occlusion, small object size, low resolution, motion blur, and poor illumination—by proposing a lightweight deployment framework. Building upon YOLOv11n, the approach incorporates domain-specific data augmentation tailored to child-centric scenarios to simulate realistic occlusions and image degradations. Furthermore, it leverages Slicing Aided Hyper Inference (SAHI) during inference to significantly enhance recall for small and occluded targets without altering the underlying network architecture. Evaluated on the Roboflow Daycare subset, the method achieves an mAP@0.5 of 0.967 and an mAP@0.5:0.95 of 0.783, representing improvements of 0.7% and 2.3%, respectively, while maintaining real-time inference capability on edge devices.

Technology Category

Computer Vision: Object Detection & CategorizationMachine Learning: Hardware-aware MLSearch and Optimization: Learning to Search

Application Category

Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applicationsSearch and Retrieval-Augmented AI: Vertical and domain-specific searchEconomics, Online Markets and Human Computation: LLM based quality controls for crowd work

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

Problem

• child detection
• noisy surveillance footage
• occlusion
• small object detection
• low-resolution video

Innovation

• domain-specific augmentation
• Slicing Aided Hyper Inference
• YOLOv11n
• child detection
• edge deployment