5 votes
Accepted

Small size datasets for object detection, segmentation and localization

There are various dataset available such as Pascal VOC dataset: You can perform all your task with these. size of the dataset is as follows ADE20K Semantic Segmentation Dataset: you can perform ...
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  • 328
5 votes
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Formal definition of the Object Detection problem

This is just an idea Given a set of pixels, the task is to decide: Which pixel is the center of an object? What is the size of the bounding boxes with the center is the pixel in part 1? Formula, ...
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  • 832
4 votes

Can YOLO detect large objects?

The example given based on the yolov1 paper: The last layer has a tensor of the dimension 7x7x30. but the dimension of the last tensor is not in every case 7x7x30. let be: S: the number of grid ...
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  • 116
4 votes
Accepted

How can I develop an object detection system that counts the number of objects and determines their position in an image?

If you want to get experience, you should probably start with some easier task. Object detection and localization are relatively hard and writing a neural network and image processing pipeline from ...
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4 votes

How does the region proposal method work in Fast R-CNN?

Yes, it is not specified because the region proposal algorithm did not change from R-CNN (the previous version from Fast R-CNN, however, in the next verion, Faster R-CNN, this algorithm is replaced by ...
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  • 1,038
4 votes
Accepted

How to add negative samples for object detection?

The quick answer: yes you can, just add images without labels, just make sure that in the negative samples there are no cars or you will make the AI crazy (i.e. convergence & instability issues). ...
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  • 1,038
4 votes
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Is it possible that the fine-tuned pre-trained model performs worse than the original pre-trained model?

Yes, this is quite the expected behavior. The main difference between expected and current behavior lies in the amount of data you are using for training VS the amount of data that the pre-trained ...
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  • 1,038
3 votes

Do models train better if the labelling information is more specific (or dense)?

It depends on what is your ultimate goal. If your goal is to simply classify the object in the image, having more complex output won't help. Simpler output representation yields better result. If your ...
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  • 1,715
3 votes

Which neural network is appropriate for measuring object dimensions from stereo images?

Is the image taken from a constant distance? If yes, you'd need to scale the images to the same dimensions first of all. For few images say 100-500 images (more the better) you'd need to label the ...
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3 votes

Is it difficult to learn the rotated bounding box for a (rotated) object?

Here's a recent paper that does what you're looking for. It looks like they achieve this simply by adding a couple rotated prior boxes and regressing the angles in between. This is similar to what ...
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3 votes
Accepted

How can I prevent the CNN from classifying a new input into one of the existing labels (it was trained with) when the input has a new different label?

You can introduce another class to your network - "not a book". After that, you will need to add new data to your dataset, random images that do not contain books to classify and train your network on ...
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  • 276
3 votes

Why do we resize images before using them for object detection?

There are different questions and even different lines of thought here. Let's go through them On resizing Why do we need to resize? To fit the network input which is fixed when nets are no Fully ...
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  • 1,038
2 votes

Is it difficult to learn the rotated bounding box for a (rotated) object?

It should not be much more difficult to predict a rotated rectangle compared to a bounding box. A bounding box can be parameterized with 4 floats: $x_c$, $y_c$, width, height. A rotated rectangle can ...
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2 votes
Accepted

How should I detect an object in a camera image?

Is this a good approach? Will I have a lot of trouble with different backgrounds? A lot will depend on the nature of the backgrounds you have, and how well they encode/decode by themselves without ...
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  • 24.5k
2 votes
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Why tf object detection api needs so few pictures?

I guess that they need so little data because their models are already trained on huge datasets, and they are just transferring the learning (using those pre-trained models as starting point).
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  • 1,098
2 votes

Which neural network is appropriate for measuring object dimensions from stereo images?

If you have stero pairs, and you can identify the objects in the scene, you do not need a neural network, you can just use triangulation. If you need to identify which objects in the scene are the ...
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2 votes
Accepted

If an image contains two distinct objects, should I create a copy of this image with distinct labels for each copy?

You should use both classes together. Let's say you use the method you proposed. Then they will be contradicting each other as one teaches the network to recognize people, not objects and the other ...
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  • 1,715
2 votes

Is it possible to train a CNN to predict the dimensions of primitive objects from point clouds?

I'm working on a similar problem. I'm using a 2D point cloud of an object, for example, X and Y coordinates for height, and with that more simple data set I will train a regression model (currently ...
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2 votes

Two Models vs One Model for Person Detection and Object Detection

Yes you can classify people bounding box with object detection. State of the art object detection model have people as one of the class in the object detection, as shown here: As you can see the ...
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  • 1,715
2 votes

Is it possible to use AI for detecting the volume of a cup

This could be possible, providing you have the right dataset to train it on. The volume of a cup consist of width, height and depth. You can probably detect all three of those given the bounding box ...
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  • 1,715
2 votes

How can I detect moving objects in a video by OpenCV without using deep learning techniques?

After a quick scan, it would seem that, in the history of object detection, machine learning has always been at the forefront. Before then, it would just be a heuristic approach. For a quick answer, ...
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  • 1,246
2 votes
Accepted

Which neural network can count the number of objects in an image?

If you want to count the number of objects using a neural network, you can use pretrained YOLO with the bottom prediction layer removed, and feed the features to a classification feed forward layer of ...
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  • 1,715
2 votes
Accepted

How to calculate the precision and recall given the predictions and targets in this case?

Precision is the number of true positives over the number of predicted positives(PP), and recall is the number of true positives(TP) over the number of actual positives(AP) you get. I used the ...
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2 votes
Accepted

What are the reasons behind slow YOLO training?

I think you underestimate the size of YOLO. This is the size of one segment of yolo tiny according to the darknet .cfg file: ...
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  • 1,246
2 votes

Object detection: combine many classes into one?

I think there is no absolute answer for this. Often its kind of trial and error. In general the CNN tries to generalize the problem, so using all logos with different augmentations and ground truths ...
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2 votes

How does YOLO handle non-class objects?

The output of YOLO is (x,y,w,h,confidence,class). The confidence value presents whether the rectangle holds an object, the rectangle is non-classed when confidence is low. The ...
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  • 1,213
2 votes
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How do I label images for deep learning classification?

If each photo is intended to show a photo of weed or crops you should give one label. If your task is different where you also try to localize weed or crops in the image, then you need to label ...
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  • 36
2 votes

How do I label images for deep learning classification?

This is really a semantic segmentation problem if OP wants to pinpoint the weeds. If OP wants to have such a segmentation he will need to hand segment every.single picture.
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