Questions tagged [object-detection]

For questions related to object detection (where objects can be e.g. humans, dogs, houses, etc.), whose meaning or definition can vary depending on the context. OD can refer to the task of locating (i.e. finding the coordinates) an object in an image (so, in this case, it would be a synonym for object localization) or the task of locating the object and classifying it (i.e. object localization + object classification).

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6 views

Dataset requirements for instance segmentation

I am creating an instance segmentation dataset for training Mask RCNN. As far as I understand I will need an Input image a bounding box annotations for each instance in the image a segmentation mask ...
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Train 3D object detection model for custom object

I am trying to train a model that can detect a 3D object and give me a 3D bounding box around it. For this, I have a RGBD camera and a 2D LiDAR. Most of the research is done for cars/cyclists/...
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How do I label images for deep learning classification?

I have roughly 30,000 images of two categories, which are 'crops' and 'weeds.' An example of what I have can be found below: The goal will use my training images to detect weeds among crops, given an ...
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26 views

Keeping track of multiple faces throughout a video

I have a video where multiple persons are seated. I need to keep track of the emotions they show throughout the video. My final result should be a csv file with all the emotions depicted by each ...
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18 views

Object detection using CNN model architectures

I've used LabelImg to create labels for my images using YOLO. After that, I would like to input the images and labels into a CNN model, like a VGG or ResNet. I've searched a lot and have not found ...
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10 views

Detect and overlay a black TV on a video

I have a short video where a TV is shown. I need to detect the TV there (let's assume it's all pure black or it's covered with a green screen), and overlay a video on top of it on the same area. I ...
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1answer
44 views

What do you mean by 'Principal Angle between subspaces'

I came across the term 'Principal angle between Subspaces' as a tool for comparing objects in images. All material that I found on the internet seem to deal with this idea in a highly mathematical way ...
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25 views

How should I label images to get high accuracy with YOLO?

I am new to Object Detection with Yolo and I have questions regarding the labeling (for custom objects): Is there any guideline or tips on how to label images to have high accuracy at the end? ...
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20 views

Faster RCNN-RPN Network Training

I am trying to understand RPN network in Faster RCNN. I understand the concept of RPN network, Pass the input images to the pre trained CNN, and get the output as feature maps Make fixed size of the ...
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9 views

Can I train an object detection model with images with a white background?

To be more specific I have a dataset of 2400 images with unbalanced classes, 1 object per image and sometimes some objects are repeated along the dataset but in a different position and rotation of ...
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20 views

Can I resize my images after labeling them?

Is it okay if I label my images with their original size and then resize them, or should I first resize them and then label them? I mean do I need to recalibrate my labels if I resized my images?
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14 views

Does the selective search algorithm in object detection learn?

I am trying to get a better grasp of how object detection works. I (almost) completely understand the concept behind RPNs. However I am little bit confused with the selective search algorithm part. ...
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32 views

High mAP@50 with low precision and recall. What does it mean and what metric should be more important?

I am comparing models for the detection of objects for maritime Search and Rescue (SAR) purposes. From the models that I used, I got the best results for the improved version of YOLOv3 for small ...
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1answer
31 views

Detect data in tables of roughly the same structure

I would like to train a model that serializes a table of nutrition facts into it's values. The tables can vary in form and colour, but always contain the same set of keys (e.g. carbs, fats). Examples ...
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50 views

What is a heatmap in the CornerNet paper?

I have been working on understanding how CornerNet works, but I couldn't figure out a few parts about the architecture. First, the authors mention that there are 3 distinct parts to be predicted as a ...
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27 views

Detect object in video and augment another video on top of it

I'm trying to detect an object in a video (with slight camera movement), and then augment another video on top of it. What is the simplest approach to do that? For instance, let's assume I have this ...
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1answer
29 views

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

I read so many articles and the Fast R-CNN paper, but I'm still confused about how the region proposal method works in Fast R-CNN. As you can see in the image below, they say they used a proposal ...
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42 views

What's the meaning of the Jaccard decay and the Jaccard recall? [closed]

I know the meaning of the Jaccard index, but when it comes to Jaccard decay and Jaccard recall I cannot see the difference or the meaning of it.
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29 views

What are some papers on face comparison in videos?

I have the following problem. Given two random frames in a video of some people, assume I can detect all faces in both frames. I would like to create a classifier that takes in pairs of faces out of ...
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1answer
53 views

What is 3D face recognition? and how we can check liveness of a face image?

Actually what is mean by 3D face recognition? In normal cases we are extracting face encoding s from a 2D image,right? Is 3D face recognition is used for liveness detection? how its possible?
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1answer
28 views

Why do we set offset (0.5) in single shot detector?

In the paper SSD: Single Shot MultiBox Detector, under section 2.2 - (4), why do we add an offset of 0.5 to x, y in generating the anchor boxes across feature maps?
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1answer
29 views

If two objects are too close to each other, would an object detector do a poor job of correctly classifying them?

Suppose we have an object detector that is trained to detect $20$ products. If two objects are too close to each other, in general, would an object detector do a poor job of correctly classifying them?...
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22 views

How come a detection works after global average pooling 2D?

I use an off-the-shelf convolutional neural network, where at the end of the convolutional part, the depth of the last convolutional layer is expanded and then its 2D average is computed (such that ...
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24 views

What are the best human re-identification methods available?

I have a use case where I need to detect, track and re-identify humans in CCTV footage. I have usedSSD and Median Flow to detect ...
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36 views

How to improve recognition of distanced objects?

I am developing a model of object detection based on fast-rcnn architecture (transfer learning) in tensorflow object detection API. My problem is that created model happens to produce very good ...
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14 views

How can I perform object detection by cutting the image into many pieces each containing one object?

Our task is to do a special object detection: In the traditional case, the neural network will output some rectangle bounding boxes. But in our case, the network should output many nearly-vertical ...
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1answer
31 views

Object detection: combine many classes into one?

I am trying to train a model that detects logos in documents. Since I am not really interested in what kind of logo there is, but simply if there is a logo, does it make sense to combine all logos ...
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28 views

Can the addition of low-quality images to the training dataset increase the network performance?

I already trained a deep neural network called YOLO (You Only Look Once) with high-quality images (1920 by 1080 pixels) for a detection task. The result for mAP and IOU were 93% and 89% respectively. ...
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1answer
52 views

Do deeper residual networks perform better or worse?

If you have an $18$ layer residual network versus and a $32$ layer residual network, why would the former do better at object detection than the latter, if you have both models are training using the ...
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31 views

Dealing with very similar object classes in object detection

I'm working on an object detection problem using Faster R-CNN. I need to identify two object classes, and they are very similar to one another. Furthermore they are similar to a third type of object ...
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29 views

How to adapt MTCNN to large images with relatively small ROIs

This question could be generalised to how to adapt state-of-the-art object detection models to large images with small ROIs. In my particular case I'm trying to use this implementation of MTCNN to ...
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2answers
107 views

What are the reasons behind slow YOLO training?

I'm testing out YOLOv3 using the 'darknet' binary, and custom config. It trains rather slow. My testing out is only with 1 image, 1 class, and using YOLOv3-tiny instead of YOLOv3 full, but the ...
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17 views

Irregular results while prediction identical object on same image

I used the pre-trained model faster_rcnn_resnet101_coco.config with my own dataset. I have two issues some objects were not detected, while I learned it, with a high number of steps, and test over ...
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20 views

CBIR and object detection

How does CBIR (content based image recognition) fit into the problem of object detection? Let's say we want to detect 4 types of dogs (Golden Retriever, Cocker Spaniel, Greyhound, and Labrador). We ...
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2answers
54 views

Is the choice of the optimiser relevant when doing object detection?

Suppose that we have 4 types of dogs that we want to detect (Golden Retriever, Black Labrador, Cocker Spaniel, and Pit Bull). The training data consists of png images of a data set of dogs along with ...
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1answer
81 views

Is there a car detection software written in Tensorflow or Keras with Python? [closed]

For a current project demo, I'm searching for a car detection neural network in Python written in TF/Keras (or any other type, as long as it has no C++ dependencies). Later on, I gonna write my own, ...
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2answers
60 views

What is the best way to detect and recognize traffic signs in a picture?

I'm working on a project for my college to recognize traffic signs in pictures. I searched a lot but can't find the best method to do it. Can someone recommend me a paper, article, or even GitHub link ...
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17 views

Similarity of Images (CBIR) for two different cameras

Suppose we have a top down picture of an object (let's say it is a shoe) from an overhead camera. Also suppose we have a database of various objects from a closeup camera. If we feed the top-down ...
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1answer
34 views

Choosing Data Augmentation smartly for different application

I'm trying to understand the role of data augmentation and how it can affect the performance/accuracy of a deep model. My target application is fire detection (on video frames), with almost 15K ...
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1answer
44 views

On which data evaluate an object detection model ? (similar or real life data ?)

I'm training an object detection model (SSD300) to detect and classify body poses in thermal images. Even I have more than 2k different poses, but the background does not change much (I have only 5 ...
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20 views

Post-classification after inference

I designed a fire detection using Deep Learning based classification approach. In my training dataset, I have both fire and fire smokes are supposed to be detected (all under "fire"; mostly real fires ...
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1answer
68 views

Possible model to use to find pixel locations of objects

I want to make a model that outputs the centre pixel of objects appearing in an image. My current method involves using a CNN with L2 loss to output an image of equivalent size to the input where ...
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3answers
478 views

Small size datasets for object detection, segmentation and localization [closed]

I am looking for a small size dataset on which I can implement object detection, object segmentation and object localization. Can anyone suggest me a dataset less than 5GB? Or do I need to know ...
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0answers
18 views

Text detection on English and Chinese language

https://arxiv.org/abs/1910.07954 In this paper, we have a convolutional character neural network where we have object detection by taking a character as a basic unit. First, we do character detection ...
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0answers
35 views

What is the expected value of an IOU in this case?

I have a detection problem. An object with a probability of 0.5 is in a box with coordinates ((0,0), (2, 2)) and with a probability of 0.5 a box with coordinates ((2,0), (4,2)). What is the maximum ...
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19 views

Finding unique faces in a video

I am trying to find unique (distinct) faces in multiple videos files. What is the best way to do that?
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1answer
36 views

How does non-max suppression work when one or multiple bounding boxes are predicted for the same object?

My understanding of how non-max suppression works is that it suppresses all overlapping boxes that have a Jaccard overlap smaller than a threshold (e.g. 0.5). The boxes to be considered are on a ...
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1answer
28 views

Evaluate precision and recall results

The following table shows the precision and recall values I obtained for three object detection models. I evaluate the first two models as the following. The target is to find the best object ...
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25 views

YOLOv3 Synthetic Data Training

Suppose we want to train a model to detect various objects. Let's say we have training data of those objects in various backgrounds along with their bounding boxes. Basically these objects have been ...
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2answers
474 views

Calculation of FPS on object detection task

How to calculate mean speed in FPS for an object detection model like YOLOv3 or YOLOv3-Tiny? Different object detection models are often presented on charts like this: I am using the DarkNet ...