Object Detection (英)

In computer vision, object detection is one of the tasks that AI shows remarkable result. Each object detection task has different models and creates different level of output. This is my wrap-up of object detection tasks.

Image Classification

Image Classification detects class from overall image input and returns class with probability.

classes = [“dog”, “cat”, “nothing”]
prediction = [ 0.8 , 0.1 , 0.15]

Models

Xception
VGG
ResNet
MobileNet
DenseNet

Model Validation

Confusion Matrix, Accuracy, Precision, Recall, F-measure

Image Classification・Localization

Image Classification with Localization creates windows to detect class from image input and returns class, probability and bounding box.

# class label
classes = [“dog”]
prediction = [ 0.8]
# Bounding Box
legend = [ “X-Position”, “Y-Position”, “Length”, Height”]
prediction = [130, 285, 100, 185]

Models

HOG
BOF
PASCAL VOC

Model Validation

mAP(mean Average Precision)、IoU(Intersection over Union)

Object Detection

Object Detection creates windows to detect multiple objects and returns those classes, probability and bounding box.

# class label
classes = [“dog”, “dog”]
prediction = [ 0.98, 0.97]
# Bounding Box
legend = [ “X-Position”, “Y-Position”, “Length”, Height”]
prediction = [130, 285, 100, 185], [130, 285, 100, 185]

Models

R-CNN
Fast R-CNN
SSD: Single Shot MultiBox Detector
Mask R-CNN
YOLO

Model Validation

mAP(mean Average Precision)、IoU(Intersection over Union)

Semantic Segmentation

Semantic Segmentation detects and provides class at pixel level. Same class is detected as the same object, so we cannot count the target of a number.

Models

U-NET
MULTISCALE
HYBRID CNN-CRF
Model Validation
IoU and per-pixel accuracy

Instant Segmentation

Instant Segmentation detects objects in the image and assigns a class at pixel level. Compared to the semantic segmentation, we can count the target of a number.