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Objects detected with OpenCV's Deep Neural Network module (dnn) by using a YOLOv3 model trained on COCO dataset capable to detect objects of 80 common classes. Object detection is a computer technology related to computer vision and image processing that deals with detecting instances of semantic objects of a certain class (such as humans, buildings, or cars) in digital images and videos. [1]
Classification, object detection, object localization 2017 [52] M. Kragh et al. Daimler Monocular Pedestrian Detection dataset It is a dataset of pedestrians in urban environments. Pedestrians are box-wise labeled. Labeled part contains 15560 samples with pedestrians and 6744 samples without. Test set contains 21790 images without labels. Images
Small object detection is a particular case of object detection where various techniques are employed to detect small objects in digital images and videos. "Small objects" are objects having a small pixel footprint in the input image. In areas such as aerial imagery, state-of-the-art object detection techniques under performed because of small ...
Object recognition – technology in the field of computer vision for finding and identifying objects in an image or video sequence. Humans recognize a multitude of objects in images with little effort, despite the fact that the image of the objects may vary somewhat in different view points, in many different sizes and scales or even when they are translated or rotated.
R-CNN has been extended to perform other computer vision tasks, such as: tracking objects from a drone-mounted camera, [3] locating text in an image, [4] and enabling object detection in Google Lens. [5] Mask R-CNN is also one of seven tasks in the MLPerf Training Benchmark, which is a competition to speed up the training of neural networks. [6]
To detect features in an image so that it can be represented by a constellation model, the Kadir–Brady saliency detector is used on grey-scale images, finding salient regions of the image. These regions are then clustered, yielding a number of features (the clusters) and the shape parameter X {\displaystyle X} , composed of the cluster centers.
Part-based models refers to a broad class of detection algorithms used on images, in which various parts of the image are used separately in order to determine if and where an object of interest exists.
Template matching [1] is a technique in digital image processing for finding small parts of an image which match a template image. It can be used for quality control in manufacturing, [2] navigation of mobile robots, [3] or edge detection in images.