CribNet: Enhancing Infant Safety in Cribs Through Vision-Based Hazard Detection

Shaotong Zhu, Amal Mathew, Elaheh Hatamimajoumerd, Michael Wan, Briana Taylor, Rajagopal Venkatesaramani, Sarah Ostadabbas

Abstract: Recent advancements in object detection and human activity recognition have shown commendable progress, albeit with a predominant focus on adult-centric applications and datasets. This paper proposes a new vision-based, infant-focused hazard detection framework, CribNet, to assess threats to in-crib safety in the form of blanket occlusions and hazardous toys, as a step towards addressing the broad, critical problem of infant sleep safety. CribNet estimates hazards by considering the proximity and characteristics of detected objects around the infants. To evaluate the framework, we created the first publicly available crib hazard detection (CribHD) dataset, consisting of 1,620 images specific to infant-centric environments. These images present a wide range of real-world challenges, including clutter, occlusion, varied lighting conditions, with and without presence of infants in the images. We show that the framework performs with over 80% mean average precision (mAP) in segmenting toys and blankets and accurately assessing hazards, marking a new advancement in infant safety. CribNet and CribHD lay the foundation for future developments in in-crib hazard detection and infant sleep safety 1 1 The code and our data are publicly available at https://github.com/ostadabbas/CribNet.