Questions tagged [object-detection]

Object detection deals with recognizing the presence of objects of a certain semantic class (e.g., “humans”, “buildings”, “cars”, etc.) in digital image and video data.

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17 views

Why am I seeing the R Error: object not found when I'm referencing the correct data and have no typos? [closed]

I'm super confused, I have my data set in the system and it says it cannot find one of my objects I'm referencing. I'm using xtabs with conditional formatting, and when I try to switch the objects it ...
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9 views

Template matching for detecting multiple objects in image (+500 different categories)

I want to detect items from the inventory of a game. Detection is in real time, so I need decent performance (> 5 FPS). There are over 500 different items, but the item icons will not change so a ...
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Summary of all the results of all participants after 2015 in ImageNet (ILSVRC) challenge. Does such resource exist?

I have been reading about the ImageNet Large Scale Visual Recognition Challenge (ILSVRC). And while I can find the paper by Russakovsky et al. from 2014(updated in 2015), which contains all the ...
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8 views

Why does strict translation invariance get destroyed after doing zero padding in the images?

While reading the paper on the Siamese Visual Tracking approach, I stumbled on this concept of Strict Translation Invariance in CNNs. The paper quotes: "Concretely speaking, one reason is that ...
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13 views

Object detection model doesn't perform well on camera frames

I am working on my thesis project, and I am a bit stuck on a problem. I am using this pretrained object detection model on a security camera, to find the b-box of objects.: http://download.tensorflow....
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21 views

Performing object detection without localization

I'm looking to build a computer vision model that can detect the presence or absence of several classes of object within an image, but the localization of these objects is not required. The end ...
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19 views

What is the difference between these error metrics in object detection

I am wondering what is the difference in these error metrics (and what they are) in object detection: mAP_0.5:0.95, mAP_0.5, recall, precision
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19 views

Yolov- 3: How are features / detection at 3 different level arranged to give 1 final output?

In yolo-v3, at layer numbers 82,94,106, detection are made. What if Same object was detected in all those places? Grids were different due to no of grids ...
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21 views

Does it help to provide training samples with no target objects in object detection?

I am training a Yolo for object detection. I was wondering if it will help to provide images with no instances of the target object present. If so, what would the VOC label of such an instance look ...
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16 views

what does it mean that the effective receptive field to be large for Region Proposal Network in Faster R-CNN?

I am trying to understand the Region Proposal Network portion (RPN) of the faster R-CNN paper. Under section 3.1, the authors mention the justification for using a 3x3 sliding network over the ...
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40 views

Mean Average Precision (MAP) object detection metric

I am reading up on object detection metrics from this github repository. I have a slight confusion regarding the precision x recall curve mentioned under the Metrics heading. It says that The ...
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20 views

Card image segmentation

I have a dataset of images with a single rectangular (credit) card in the middle. My goal is to filter out the pixels of the card. I would say the particular difficulties of this task are that the ...
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18 views

Yolo model with high recall, low precision

I have made an object detection model(Yolo) and being new to the whole concept I am a bit confused about how to interpret my results. So my Map is .70, Recall is quite high(.90) and precision is low(....
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27 views

Object detection metrics - AP or something better?

I am using a yolov3 network for object detection and I am having trouble determining what's the metric that evaluates well performance. At the moment I am calculating average precision and it seems ...
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25 views

CNN Color Invariance

To detect a specific object that can have any color, if we train our model with images containing that object with just a few colors (e.g. 2 or 3 colors), will the performance of our model's ...
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34 views

Improve accuracy of MobileNetv3-SSD training with TFODAPI

I'm re-training MobileNetv3-SSD (pre-trained on COCO) from TF Model Zoo for the only class "person", taking the images of COCO 2017 that contains people (train set). Validatin on COCO 2017 ...
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46 views

Comparing mAP with accuracy?

I am very new to Machine Learning and wanted to ask question about comparing metrics across two types of models. Object detectors like YOLO and Instance Segmentation models like SOLO use mean Average ...
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36 views

YoloV3: Training set, true label boxes vs anchor boxes

I understand the anchor boxes as a set of boxes that based on a comprehensive exploration of the data ( all the true bonding boxes) best describe the variable sizes of all true boxes in the training ...
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30 views

How to model the probability of detecting an image, given it is seen multiple times

Are there any existing methods/models describing the probability of an object being detected by a computer vision algorithm given it is seen $n$ times at similar angles and orientations? I know that ...
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47 views

What will be the Precision and Recall value for Faster RCNN?

I am using TensorFlow object detection API for Faster RCNN object detector. Now I want to measure the performance of my model, so I have evaluated it using the code below for getting the mAP, ...
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30 views

Is there any statistically meaningful definition for object confidence in object detection?

Most modern object detection algorithms rely on neural networks and output a bounding box and confidence for each object (or more accurately, a confidence for each possible object class considered, ...
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12 views

How to treat (label and process) border cases in machine learning?

In every computer vision project I struggle with labeling guidelines for border cases. Benchmark datasets don't have this problem, because they are 'cleaned', but in real life unsure cases often ...
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337 views

What are "Grids" and Detection at different scales" in YOLOV3?

I've recently started working with Yolov3 and the more I go in depth, the more confused I get. In the simplest terms what I think about YOLOV3 ...
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8 views

Can I conclude from object detection metrics on temporal behavior of a detection system?

Assuming I know typical object detection metrics like precision, recall etc. for a certain class, is there anyway to conclude on the probability that an object of that class which is present in ...
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16 views

Is cosine distance applicable for multiple classes?

Recently I learnt about DeepSort algorithm which use deep cosine metrics to re-identify objects. However, to my understadning, ...
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11 views

Does TensorFlow's Object Detection API models look at the whole image or only the bounded target?

I was wondering if CNNs, specifically the models/feature extractors offered in Tensorflow's Object Detection API, only train on the bounded box of the target image or if it considers the entire image ...
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Evaluating the performance of tracking multiple objects detected with object detection

I have a ground truth dataset where the objects have been manually annotated and each object have been provided an ID that is consistent through time. There are no false positives or false negatives ...
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229 views

What is the average precision in the case of no positives for a given category in the context of object detection

In attempting to calculate the average precision of an object detection model, I am wondering about an edge case. Suppose at evaluation time that for a given category, that no detections of that ...
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139 views

When computing mAP for an object detection model, how many detections should one consider?

I am trying to write some code to evaluate the MS COCO style mAP (mean average precision, average computed at the category level) at different IOU levels in the context of object detection with a ...
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89 views

Interpretation of Precision and Recall from Object detection API?

In given picture I have precision and recall value -1.000. What does this signify? Could someone please help me to interpret this results? Furthermore Can I calculate F1 score for this average ...
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11 views

What are the metrics to deciede if a dataset is good enough for the deep learning task of object detection task?

I am asked by my supervisor to identify certain metrics with which we could definitively say that a dataset is suitable for the deep learning task of object detection.I did some research and found ...
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5 views

pretrained model question

I'm quite confuse about the pretrained model. Let say I'm training the rcnn or cascade rcnn. They have the backbone for classification. like resnet 50 or resnet 101 If I want to make a pretrained ...
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160 views

Two True Positive for one ground truth in object detection

I am wondering is it possible to have two true positive predictions for one bounding box ground truth only. Following this section from Stanford. They define truth positive like this: We start with ...
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62 views

Enemy detection in 3D video games without labeled dataset

I'm working on a tool for single-class object detection in video games, which should be able to detect enemies in 3D environments. The main problem is, that I cannot use any labeled dataset. Instead I ...
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19 views

Object detection vs segmentation?

My problem statement is as follows: "Object detection is the concept of classifiying & localising an object in an image, and semantic segmentation is the concept of labeling each pixel to a ...
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16 views

Evaluate result of a Detection algorithm based on the object's shape as Ground truth

I'm working on project where i have to detect small colored cars driving on rails from static camera (Bird's Eye-view see below image) The algorithm in short outputs first a mask image with only the ...
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8 views

How many Test-videos are needed to evaluate a detection algorithm?

I've developed an object-detection algorithm based on combinations of available approaches in the literature (using classical approches). The algorithm is designed for a special application which ...
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1answer
128 views

Resize image in object detection task of computer vision

In object detection, they usually resize by keeping the ratio the same as the original image, which usually names "letterbox" resize. My question is: Why we need to do that? As I see with ...
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18 views

What activation and where to use in MaskRCNN RPN

so I've been trying to implement my own version of MaskRCNN, and I am baffled by how the RPN is implemented in various places. Assuming the standard RPN architecture of a shared 3x3 Conv2d, and two ...
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143 views

Faster RCNN for one class in object detection

Let say I have a task to detect the bounding box of one object only. And the only thing I care about is the IoU between prediction and ground truth, no need for real-time. My question: Should I ...
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370 views

How many images with bounding boxes are in the ImageNet object detection dataset?

I am trying to understand how many images with object-detection bounding boxes are in ImageNet dataset, and how many objects are there in these images. In MS-COCO paper, it says: "Recently, a ...
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33 views

Should I keep training samples without labels

I want to supervisely train an object detector for pedestrians, this detector is expected to ideally detect all the pedestrians in an image, and output the detected pedestrians as 2D bounding boxes, ...
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598 views

how to find mean average precision of object detection algorithms

To start with, I would like to mention another question which was asked in a better way. But my problem differs. pseudo code for the algorithms I have four different object detection algorithms which ...
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40 views

Why Anchor Boxes in Object Detection

What I understand from the anchor boxes is that they are predefined set of boxes of different sizes and shapes. A model like YOLO predicts offsets of the coordinates for each anchor box along with ...
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179 views

How to measure the performance of Mask RCNN model. Given that there are two tasks , one object detection and another image segmentation

Mask RCNN is an instance image segmentation technique. It is based on Faster RCNN for object detection and an additional mask operation is performed by another CNN.
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1k views

1 neuron BCE loss VS 2 neurons CE loss

I built a custom version of YOLO that should only detect one type of objects, where the objectness measure (which tells how likely a bounding-box contains an object of any type), is learned using a ...
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45 views

Object Classification vs Object Detection

My problem statement is as follows: I've got some technical documents as JPEGs, mostly text, but with some standard pictograms. Each image does not need to have all the pictograms or any pictogram at ...
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84 views

yolo v1: how gradients in grids with no objects overpower

I am reading yolo v1 paper. I am having confusion while trying to understand this: "Also, in every image many grid cells do not contain any object. This pushes the “confidence” scores of those ...
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201 views

yolo v1: why we weigh localization error more than classification error

I'm reading yolo v1 paper. I am trying to understand this part of the paper: "We use sum-squared error because it is easy to optimize, however it does not perfectly align with our goal of ...
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48 views

Which Object detection model will give the best result on images when the speed is not a problem for Text Images

I want to develop a model for cropping the equations from the Maths questions as people like me are struggling a lot for doing it manually for the research purpose. I want to know if we can do this? ...