Computer Vision at the Edge on Embedded Systems Development

Image Classification

Mon, 01 Jan 0001 00:00:00 +0000

Image Classification#

Image classification assigns a single label to an entire image from a fixed set of classes, producing top-K predictions with associated confidence scores. A model trained on ImageNet outputs a 1000-element probability vector; the highest-scoring entry is the predicted class. On edge devices, the challenge is achieving useful accuracy within severe compute, memory, and power budgets — which has driven the development of model families specifically architected for efficient inference on ARM cores and neural accelerators.

Object Detection

Mon, 01 Jan 0001 00:00:00 +0000

Object Detection#

Object detection localizes and classifies multiple objects within a single image, producing a set of bounding boxes, each with a class label and confidence score. Unlike image classification, which assigns one label to the entire frame, detection answers both “what” and “where” for every object of interest. This makes it the foundation for camera-based counting, tracking, safety monitoring, and autonomous navigation on edge platforms. The computational cost is substantially higher than classification — a detection model must evaluate spatial features at multiple scales and apply post-processing to filter and deduplicate predictions — making model selection and pipeline optimization critical for real-time performance on constrained hardware.

Semantic Segmentation

Mon, 01 Jan 0001 00:00:00 +0000

Semantic Segmentation#

Semantic segmentation assigns a class label to every pixel in an image, producing a dense class map rather than sparse bounding boxes. Where object detection answers “what objects are present and roughly where,” segmentation answers “which class does this exact pixel belong to.” This pixel-level precision is essential for tasks like autonomous navigation (identifying drivable surface vs obstacle at the boundary), agricultural robotics (distinguishing crop from weed at the stem level), and industrial inspection (measuring defect area in square millimeters rather than bounding box approximations).

Pose Estimation

Mon, 01 Jan 0001 00:00:00 +0000

Pose Estimation#

Pose estimation localizes body keypoints — joints, facial landmarks, or extremities — within an image, producing a skeleton that describes a person’s body configuration. Unlike object detection, which outputs a bounding box around the whole person, or semantic segmentation, which labels every pixel as “person” or “not person,” pose estimation outputs the spatial coordinates of anatomically meaningful points: shoulders, elbows, wrists, hips, knees, ankles, and others. This enables applications that depend on understanding body posture — fitness form analysis, gesture control interfaces, fall detection in elder care, and sign language recognition.