Image classification vs object detection TinyML has made great progress in image classification, where the machine learning model must only predict the presence of a certain type of object in an ...

Detection of “indeterministic” defect types such as cracks and/or scratches is quite challenging since such defects may have a variety of shapes, locations and severity. Deep learning, a subfield of ...

Image classification is one of AI's most common tasks, where a system is required to recognize an object from a given image. Yet real life requires us to recognize not a single standalone object but ...

Object detection takes image classification one step further and provides the bounding box where detected objects are located.

At the same time, advances in embedded AI for object detection and categorization such as YOLO, GoogleNet and AlexNet reached an unprecedented level of accuracy (mean-Average Precision – mAP) and ...

Object detection is a useful adjunct improvement to image classification as it can, for example, identify a person and a dog in the same image, rather than just classify an image of a dog as a dog, as ...

These systems depend heavily on data annotation to train AI/ML models that enable real-time perception and decision-making. Raw data is collected from a suite of sensors, including LiDAR, radar, ...