This paper proposes HaloBus, an innovative, edge‑computing solution designed to mitigate this risk by detecting student boarding and exiting in real time using lightweight AI based methods. A persistent challenge in elementary school transportation is the issue of missing students after they exit their buses, which disproportionately impacts low-income households. Current safety systems are forced upon the individual households with their own methods. Common methods include applications on a personal device or a small tracker. However, not everyone can afford these options, and every parent deserves to feel like their child is safe. That is why HaloBus was invented. The system employs YOLOv5u—an Ultralytics‑enhanced, anchor‑free, split‑head architecture that offers a superior accuracy–speed tradeoff. By providing real-time, on-device alerts, HaloBus enables immediate intervention to prevent a student from being left behind, thereby shifting the focus from reactive post-incident response to proactive safety. Trained on over 70,000 labeled and unlabeled images, the model can accurately detect multiple students simultaneously, significantly reducing false positives. In real‑world deployment, the model sustained 30 frames per second on the Raspberry Pi and achieved detection confidence levels exceeding 75% even when subjects wore sunglasses or hoodies. With opt‑in participation for each family, HaloBus effectively balances detection efficiency and privacy protection. Overall, HaloBus offers a low‑cost, scalable, and ethically conscious approach to enhancing school‑bus safety by delivering reliable, on‑device boarding and exit detection for multiple students in varied real‑world conditions.