Is Semantic Segmentation more accurate than Object Detection?

Is Semantic Segmentation more accurate than Object Detection for samll object?

My application is small defect detection and the following image is an example of defect on circuit.

Can you please advice the Pros and Cons about using Semantic Segmentation and Object Detection?

Speed? Accuracy?

Answer 1

[Mahesh Taparia]

Hi Chuan,

Semantic segmentation gives a pixel level classification in an image, i.e. it classifies the pixels into its corresponding classes, whereas object detection classifies the patches of an image into different object classes and create a bounding box around that object. So, it depends on problem statements to select an approach.

Using Segmentation: In this case it will be a two-class problem: region with defect/ without defect. If the pixel information of defected and non-defected region is same, then there is a chance of more misclassification. Moreover, preparing training dataset is for this problem is a challenging and time-consuming task.

You want to detect the defect in a circuit board, it will be better if you go with object detection algorithms as it will take the patches from an image and classifies it into defected and non-defected regions.

Hope it will help.

[Jwan Alwan]

@Mahesh Taparia can you explain more why it it is challenging and time consuming task

[Mahesh Taparia]

Hi

For segmentation problem, you need labels for each pixel in an image. Labeling each pixels is time consuming and challenging. However, these problem can be reduced if there is any labeling automation algorithm. For more information, you can check this documentation.