Object detection generally is categorized into 2 stages: Today’s deep learning-based techniques vastly outperform these.ĭeep learning-based approaches use neural network architectures like RetinaNet, YOLO (You Only Look Once), CenterNet, SSD (Single Shot Multibox detector), Region proposals (R-CNN, Fast-RCNN, Faster RCNN, Cascade R-CNN) for feature detection of the object, and then identification into labels. These would detect a number of common features across the image, and classify their clusters using logistic regression, color histograms, or random forests. Common ones included viola-jones object detection technique, scale-invariant feature transforms (SIFT), and histogram of oriented gradients. Types and modes of object detectionīefore deep learning took off in 2013, almost all object detection was done through classical machine learning techniques.
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💡 Pro tip: Looking for the perfect tool for building object detection models? Check out 13 Best Image Annotation Tools of 2022. Within this branch of detectors, instance segmentation models will do a better job at understanding and segmenting occluded objects than mere bounding-box detectors. Occlusion is handled far better in two-stage detection networks than one-shot approaches. Objects that are often occluded-Use Instance Segmentation if possible Object detection will still pick up the “sky” as an object, but it will struggle far more with such objects. Semantic segmentation is more efficient at “painting” pixels that belong to these classes. The sky, ground, or vegetation in aerial images don’t really have a defined set of boundaries. Objects that have no clear boundaries at different angles-Use semantic segmentation Things in an image such as the tag “sunny”, “bright”, or “skewed” are best identified by image classification techniques-letting a network take the image and figure out which feature correlate to these tags. Objects that have no physical presence-Use classification Picture: A diagonal pencil labeled on V7 using box and polygon This biases model towards background pixels rather than the object itself. Long and thin items such as a pencil will occupy less than 10% of a box’s area when detected. Objects that are elongated-Use Instance Segmentation. You have to always ask yourself: Do these scenarios apply to my problem?Įither way, here's a cheat sheet you can use when choosing the right computer vision techniques for your needs. However, it is outclassed by other methods in other scenarios. Localizing objects at high speed (>15fps).Detecting clusters of objects as 1 item.Detecting objects with clear boundaries.Detecting objects that take up between 2% and 60% of an image’s area.
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