I have a working object detection model (fined-tuned MobileNet SSD) that detects my custom small robot. I'll feed it some webcam footage (which will be tied to a drone) and use the real-time bounding box information.
So, I am about to purchase the camera.
My questions: since SSD resizes the input images into 300x300, is the camera resolution very important? Does higher resolution mean better accuracy (even when it gets resized to 300x300 anyway)? Should I crop the camera footage into 1:1 aspect ratio at every frame before running my object detection model on it? Should I divide the image into MxN cropped segments and run inference one by one?
Because my robot is very small and the drone will be at a 4 meter altitude, so I'll effectively be trying to detect a very tiny spot on my input image.
Any sort of wisdom is greatly appreciated, thank you.
These are quite a few questions, I'll try to answer all of them. The detection model resizes the input images before feeding it to the network by some resizing method, e.g. bilinear. It would be better of course if the input image would be equal or larger than the input size to the network rather than smaller. A rule of thumb is that indeed higher resolution means better accuracy, but it highly depends on the setup and the task. If you're trying to detect a small object, and let's say for example that the original resolution is 1920x1080. Then after resizing the image, the small object would be even smaller (pixels-wise), and might be too small to detect. Therefore, indeed, it would be better to either split the image to smaller images (possibly with some overlap to avoid misdetection due to object splitting) and applying detection on each, or using a model with higher input resolution. Be aware that while the first is possible with your current model, you'll need to train a new model possibly with some architectural changes (e.g. adding SSD layers and modifying anchors, depends on the scales you want to detect) for the latter. Regarding the aspect ratio matter, you mostly need to stay consistent. It doesn't matter if you don't keep the original aspect ratio, but if you don't - do it both in training and evaluation/test/deployment.
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I am using the tensorflow object detection API for the object detection task. However, I have objects that are captured from a high angle (camera at 10 m) and in a very small size where the size of images is 1920 x 1080.
Questions:
1) What is the best way to detect small objects under this condition?
2) What are the features of suitable dataset? Images from the same views (maybe!)?
I appreciate all of your answers, Thanks :)
You have to consider object detector's input size, even if you use high resolution image such as 1920x1080.
Because object detector resize input image to their architecture size(ex. general YOLO use 410x410 input in their architecture)
On the other hand, if you use 1920x1080 image as it is, your API will resize it to small resolution like 410x410.
It means your small objects in images will be disappeared during passing through convolution filter.
In my opinion,
1) If you know where small objects is located in whole image, CROP&SEPARATE image and USE as an input image.
Even though you do not know where small objects is, you can make several candidate list that is separated by some method.
2) I don't understand what you want to know, please let me know more specific.
I think you should try "faster_rcnn_resnet101" model with kitti dataset, this has the max image size of 1987. but this model is very slow compared to any other SSD models. The configuration link is below -
https://github.com/tensorflow/models/blob/001a2a61285e378fef5f45386f638cb5e9f153c7/research/object_detection/samples/configs/faster_rcnn_resnet101_kitti.config
Also the Faster rcnn models do better job compared to yolo in small object detection, not sure of performance with ssd model.
I am trying to build CNN model using TensorFlow at my own data set. But i faced with problem that is i have many pictures with different sizes. There are one kind of object in my pictures. If i make all pictures with same size, objects at pictures are not same size. In order to run CNN model with TensorFlow how to fix this problem? I heard one thing from others that is no matter having different size of input data, using tf.reduce_max, tf.reduce_mean is the best solution. if it is true that best solution to fix my problem, how to use this in my CNN model?
If i make all pictures with same size, objects at pictures are not same size.
If you know already how to make your input images to have the same size, you are ready for your task to train your CNN model. Unless you have a strict need to make the object for the picture to have the same size, it does not matter to the network.
Usual approach is to resize the images to a fixed size that is accepted by the network as input. This means distorting the aspect ratio of objects.
If that bothers you, you could try padding the images to a square (supposing the network input is a square) and then resize. This will keep the aspect ratio, but add some extra-information (the padding).
Another option is to crop the image to a square, if you are confident you are not losing important information and your task allows it.
The Tensorflow Object Detection API offers a variety of models. These are trained at 600x600 image size. Suppose I have a 6000x4000 satellite image, and I want to detect objects continuously throughout the image. What is the best practice for adapting a TFODI model to this image size? I don't care about the running time per image for object detection. I have a GPU with 9GB of RAM.
I know I can fit a single 6000x4000 image onto this GPU. I'm not sure if I can fit an image processing neural net for that size onto the GPU. I can think of a few alternatives:
Chip the image into 600x600 blocks, which risks losing features that cross the blocks, but then everything should work out of the box.
Change the image dimensions in the model definition from 600x600 to 6000x4000. Can I retrain from the Model Zoo checkpoint, or do I have to start from scratch if I do this?
Compress the image to smaller size. This distorts the image dimensions and also loses feature detail. For say a picture of a city, the resulting detail would not be adequate to pick out cars and small houses.
You need to try with different sizes and see using what size during training you don't run out of memory. The memory consumption also depends how many images you have that you are training on. From what you described you will end up using a intermediate size of the image
I am training my own image set using Tensorflow for Poets as an example,
https://codelabs.developers.google.com/codelabs/tensorflow-for-poets/
What size do the images need to be. I have read that the script automatically resizes the image for you, but what size does it resize them to. Can you preresize your images to this to save on your disk space (10,000 1mb images).
How does it crop the images, does it chop off part of your image, or add white/black bars, or change the aspect ratio?
Also, I think Inception v3 uses 299x299 images, what if your image recogition requires more detailed accuracy, is it possible to increase the networks image size, like to 598x598?
I don't know what re-sizing option this implementation uses; if you haven't found that in the documentation, then I expect that we'd need to read the code.
The images can be of any size. Yes, you can shrink your images to save disk space. However, note that you lose image detail; there won't be a way to recover the lost information.
The good news is that you shouldn't need it; CNN models are built for an image size that contains enough detail to handle the problem at hand. Greater image detail generally does not translate to greater accuracy in classification. Doubling the image resolution is usually a waste of storage.
To do that, you'd have to edit the code to accept the larger "native" image size. Then you'd have to alter the model topology to account for the greater input size: either a larger step-down factor somewhere (which could defeat the greater resolution), or another layer on the model to capture the larger size.
To get a more accurate model, you generally need a stronger network topology. 2x resolution does not give us much more information to differentiate a horse from a school bus.
I'm attempting to train a faster-rccn model for small digit detection. I'm using the newly released tensorflow object detection API and so far have been fine tuning a pre-trained faster_rcnn_resnet101_coco from the zoo. All my training attempts have resulted in models with high precision but low recall. Out of the ~120 objects (digits) on each image only ~20 objects are ever detected, but when detected the classification is accurate. (Also, I am able to train a simple convnet from scratch on my cropped images with high accuracy so the problem is in the detection aspect of the model.) Each digit is on average 60x30 in the original images (and probably about half that size after the image is resized before being fed into the model.) Here is an example image with detected boxes of what I'm seeing:
What is odd to me is how it is able to correctly detect neighboring digits but completely miss the rest that are very similar in terms of pixel dimensions.
I have tried adjusting the hyperparameters around anchor box generation and first_stage_max_proposals but nothing has improved the results so far. Here is an example config file I have used. What other hyperparameters should I try adjusting? Any other suggestions on how to diagnose the problem? Should I be looking into other architectures or does my task look doable with faster-rccn and/or SSD?
In the end the immediate problem was that I was not using the visualizer correctly. By updating the parameters for visualize_boxes_and_labels_on_image_array as described by Johnathan in the comments I was able to see that that I am at least detecting more boxes than I had thought.
I check your config gile, you are decreasing the resolution of your image to 1024. The region of your digit will not contain a lot of pixel and you are loosing some information. What I suggest is to train the model with an another dataset (smaller images). You can for example crop the images in 4 four area.
If you have a good GPU increase the max dimension in the image_resizer, but I guess you will run out of memory