OBJECT DETECTION
What is Object Detection ?
Object detection may be a computer vision technique for locating instances of objects in images or videos. Object detection algorithms typically leverage machine learning or deep learning to supply meaningful results. When humans check out images or video, we will recognize and locate objects of interest within a matter of moments. The goal of object detection is to duplicate this intelligence employing a computer.
Object detection may be a key technology behind advanced driver assistance systems (ADAS) that enable cars to detect driving lanes or perform pedestrian detection to enhance road safety. Object detection is additionally useful in applications like video surveillance or image retrieval systems.
Architecture Overview
R-CNN, Faster R-CNN, Mask R-CNN
A number of popular object detection models belong to the R-CNN family. Short for region convolutional neural network, these architectures are supported the region proposal structure discussed above. Over the years, they’ve become both more accurate and more computationally efficient. Mask R-CNN is that the latest iteration, developed by researchers at , and it makes an honest start line for server-side object detection models.
YOLO, MobileNet + SSD, SqueezeDet
There also are variety of models that belong to the only shot detector family. the most difference between these variants are their encoders and therefore the specific configuration of predetermined anchors. MobileNet + SSD models feature a MobileNet-based encoder, SqueezeDet borrows the SqueezeNet encoder, and therefore the YOLO model features its own convolutional architecture. SSDs make great choices for models destined for mobile or embedded devices.
More recently, researchers have developed object detection models that do away with the necessity for region proposals entirely. CenterNet treats objects as single points, predicting the X, Y coordinates of an object’s center and its extent (height and width). this system has proven both more efficient and accurate than SSD or R-CNN approaches.
How Vancouver Automation can help you ?
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