Real-Time Object Detection on Mobile in AI & Artificial Intelligence

Traditional convolutions are computationally 'Expensive' because they combine spatial information and channel information in a single 3D filter. MobileNet revolutionized edge vision by splitting this into two parts: a Depthwise Convolution (spatial filtering) followed by a Pointwise Convolution (channel combination). This mathematical trick reduces the number of parameters and multiplications by nearly 90% while maintaining enough expressive power to identify hundreds of object classes in real-time on a standard smartphone.

For real-time video, we cannot use 'Two-stage' detectors that first propose regions and then classify them. Instead, we use Single-Shot architectures like SSD or YOLO. These models look at the image once, dividing it into a grid and predicting both bounding box coordinates and class probabilities simultaneously. When combined with Post-Training Quantization and a GPU Delegate, these models can reach sub-20ms inference times, enabling 60 FPS applications that feel fluid and alive to the user.

Can your phone see as well as a human? In this lesson, we'll master Real-Time Object Detection on Mobile—exploring the architectures and optimizations that make 60 FPS vision possible.

Standard object detectors like Faster R-CNN are too slow for mobile. We use Single-Shot Detectors (SSD) or YOLO, which process the whole image in a single pass.

The key is MobileNet's 'Depthwise Separable Convolutions'. These split a standard convolution into two simpler steps, reducing the math by 8-9x with minimal accuracy loss.

Checkpoint: Why is MobileNet so much faster than a standard VGG or ResNet on a mobile phone?

• →It has fewer layers
• →It uses Depthwise Separable Convolutions which drastically reduce the number of multiplications needed

To achieve 60 FPS, we must use the GPU delegate. This allows the phone to handle the camera stream and the AI inference simultaneously without heating up.

By mastering Mobile Vision, you've learned to build apps that understand the physical world in real-time. You're ready to create the next generation of AR and smart assistants.

Checkpoint: True or False: In real-time mobile object detection, it is common to process a smaller, downsampled version of the camera frame (e.g. 300x300) to maintain speed.

• →True
• →False

Vision mastered! Now, let's listen for the edge. In our next lesson, we'll master Wake Word Detection for voice assistants.

Next, we'll explore Audio Edge AI—detecting 'Hey Siri' or 'OK Google' with ultra-low power.

Check a Real Real-Time FPS Budget. Finish checking whether an inference is fast enough to hit a target frame rate.