YOLO AI Object Detection in DriveSight

April 2026 · 5 min read

Most "AI dashcam" features mean the footage gets sent to a server somewhere, analyzed remotely, and the results come back over the internet. DriveSight's AI detection works completely differently: the neural network runs on your phone, on your footage, without touching any server.

Here's a look at what it detects, how the model works, and why running AI on-device matters for privacy and reliability.

What Gets Detected

The YOLO model in DriveSight is trained on the standard COCO dataset with additional fine-tuning for dashcam-specific scenarios. It detects objects across four main categories:

When the model detects an object, a labeled bounding box appears on the dashcam feed in real time. Each box shows the object class and the model's confidence score. Detections appear and disappear frame by frame as objects enter and leave the camera's view.

How YOLO Works on Your Phone

YOLO — which stands for You Only Look Once — is a neural network architecture designed specifically for real-time object detection. Unlike older detection methods that scanned an image multiple times at different scales, YOLO processes the entire frame in a single pass through the network. That's what makes it fast enough to run on mobile hardware.

DriveSight uses a compact version of YOLO optimized for mobile inference. The model file is bundled inside the app. When you enable AI detection, the app loads the model into memory and begins passing camera frames through it at a fixed interval.

On phones with a dedicated neural processing unit (NPU) — like Snapdragon 8-series, Google Tensor, or Dimensity 9000+ chips — the model runs through the NPU rather than the CPU, which is faster and more power-efficient. On older hardware without an NPU, the model falls back to GPU or CPU inference. The detection still works; it's just slightly slower.

Why It Does Not Flag Deer

Deer collisions cause roughly 1.5 million accidents in the US each year, with peak risk at dawn and dusk when visibility is lowest. A deer can cross a road faster than most drivers can react — from the moment a deer enters headlight range to impact is often under a second.

This is the case for deer detection, and DriveSight does not make that claim. Wild-animal classes were removed from the model because they fired on trees and bushes, and a dedicated deer model was disabled after it labelled cats and whole rooms as deer. A warning you learn to ignore is worse than none.

What DriveSight ships for deer season instead is the spoken wildlife hotspot warning, which works from reported sightings and your GPS position rather than the camera, so darkness and blind curves do not defeat it. The full reasoning is in the deer detection guide.

Privacy: Why On-Device Matters

The standard approach for AI features in consumer apps is to send data to a cloud server, process it remotely, and return results. This is simpler to build and typically more accurate (cloud hardware is faster). But it means your footage, or frames from it, leave your device.

DriveSight's AI runs entirely on-device for a simple reason: your footage stays yours. No frames are ever transmitted to any server. The model doesn't need an internet connection to run. If your phone has no SIM card and no WiFi, AI detection works exactly the same.

The privacy policy is explicit: AI detection runs on a local YOLO model. No video is ever sent to any server. The model itself is bundled in the app — there are no API calls to any AI service.

Enabling AI Detection

AI object detection is a DriveSight Pro feature. To enable it:

1. Open DriveSight and go to Settings
2. Under Pro Features, tap AI Object Detection
3. Toggle it on — the model loads into memory (takes a few seconds the first time)
4. Start a drive session — bounding boxes appear on the live camera feed as objects are detected

Enabling AI detection increases CPU and GPU usage compared to recording alone. On most phones this has no effect on recording quality. On very old or low-end hardware, if detection is slow, the app automatically reduces inference frequency to avoid dropping recording frames.

Frequently Asked Questions