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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11 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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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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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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20 views

Yolo for object detection

I am working on (RSNA Pneumonia Detection Challenge) of kaggle. In which we have to find if a patient is suffering from pneumonia and if suffering then find the abnormality in the image by finding ...
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Unsupervised Face Clustering [closed]

I want to detect faces appearing in front of webcam into individuals with anonymized labels like “Person A”, “Person B” so on. Here is my code --> ...
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36 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? ...
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Faster R-CNN : How to change max pool to average pool in pre-trained tensor-flow object detection model

I am using pre-trained faster R-CNN tensor flow object detection API for my use case. I want to change ROI pooling layer from existing max pool to average pool, how can I do that. Is it possible to do ...
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7 views

How to evaluate (compare) probmaps?

In the field of Object Detection where bounding boxes is the output I suppose comparing results is easy, but with the problem of Road detection, in which the output is usually Probmaps, how do you ...
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Why is SSD object detection model overfitting even with very high weight decay?

Actually I am struggling for a long time with this problem and had tried a lot of experiments. I am working on an object detection model using the SSD architecture with various backbones. I started ...
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Estimating epistemic uncertainty in object detection

I've begun to explore a dataset wherein I am determining whether an object is present, after making N attempted indep. observations of the same object's position. While N attempts may be made, the ...
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Can I use the sigmoid activation function to predict bounding box coordinates?

I’m designing a network which localizes images of individual fruit belonging to one of six different classes of fruit. The target bounding box parameters are normalized so that they assume values ...
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What is the difference between HLC (Histogram of local features) , CSS ( color self-similarity) ans MDST (Max DisSimilarity of Different Templates)

I'm new to computer vision and have been researching for Master thesis purposes in Detection algorithms and the techniques used in each. As I arrived to the point where alot of papers showed the ...
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How deep does the following convolutional neural network need to be?

Over the course of the last two days, I’ve trained two convolutional neural networks, The first of these networks comprised of 3 sets of one convolutional layer, followed by a max pooling layer, and ...
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Do we need to back propagate through non-max suppression layers?

In convolutional networks designed to serve the purpose of object detection, following the output of the final activations in the network, said activations are processed in accordance with the theory ...
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Need advice improving object detection model

i have 433 image dataset which consists of class (multiple class can be inside one image): 'a': 634, 'b': 501, 'c': 590, 'd': 293, 'e': 524, 'f': 262 i know my ...
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Equivalence of maP and confusion matrix in object detection

I have a piece of code that computes maP for object detection using this approach I have another piece of code that computes a confusion matrix for object detection using this approach If I then use ...
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Resize images before training object detection

I am training an object detector. I didn't resize my image before labeling because the of assumption that the model does this automatically to fit its input shape. ...
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Will running many classification models sequentially be similar to running only 1 detection model?

This question is related to this other one: In what situation many classification models (specialized) will be better to use instead of just 1 (generalist)? With 1 classification model it is possible ...
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In what situation many classification models (specialized) will be better to use instead of just 1 (generalist)?

Recently, I started working on Classification and Object detection. I am using a dev board to make ONLY inference. So I am just using the models created by someone else. As I have to develop a demo, ...
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Invoice Extraction

What topics should I read on to prepare myself for data extraction from invoices (in image file or PDF). Basically I will have a file with invoice and need to extract from it such information as ...
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Acceptable level of mAP in computer vision applied to health applications

I am trainig convolutional methods for detecting dental features in radiographs and trying to get the highest mAP by fine tuning, training new models and improving the ground truth labels by paying ...
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In the case of YOLO, how does the network assign a box in it's grid based on the midpoint of the object?

My question is that how in YOLO, the networks does the midpoint of grid cell think ? I'm not completely sure I understand it. How can we know the midpoint of any object before actually knowing where ...
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Finding the number of true positives, true negatives, false positives and false negatives returned by a detector?

This is just example information. If I run a detector on a video sequence to detect a certain object and I am given the number of object detections returned by a detector (72), The actual number of ...
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Object detection mAP bias from recall/precision edge case

Standard metrics used in Pascal VOC or COCO samples recall over n steps from 0 to 1. In a give example, best precision values are obtained for relatively low recall ~0.45 and carried up to 0. Thus, ...
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Algorithms for sonar segmentation

I am looking at trying to create an autonomous vehicle that relies on sonar for awareness. I’ve got a rig fitted out with some servos and ultrasonic sensors. Now I need to try and devise an algorithm ...
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Will missing labels in object detection cause degradation of accuracy?

Say if I need object detection of peanuts (say using masked-rcnn), and for all training images with lots of peanuts, I only annotate 3-4 peanuts, would those unlabeled peanuts have negative impact on ...
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What is the error for ImageNet “Object localization” challenge?

I have been reading some papers which use the ImageNet-LOC (ImageNet-Localization) dataset. I tried to read up on it to understand what the goal of this dataset is, and hence, what the error we are ...
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How to use Yolo Darknet in C++ applications? [closed]

I am unfortunately very inexperienced with C++ and have only used darknet in Python so far. For the work I have to set up a C++ project with Visual Studio, in which Yolov3 recognizes objects in the ...
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Best way for robust hand tracking on PI 4

I need a robust hand tracking which should be running on a Raspberry Pi 4 model. Does anyone have some experience with that? One way could be to use OpenCV but I think it's just using the skin color ...
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Define False Positive and False Negative on Object Detection and Recognition

I'm experimenting on object detection and recognition, though other words may refer to this as object localization and classification. Both localization and classification are often grouped together ...
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Bilinear interpolation concept: Error in PyTorch implementation?

I am studying the ROIAlign concept. This is a submodule of an object detection CNN architecture like Faster-RCNN. Basically it is about the following: Given a 'region of interest' of a varying size in ...
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How to standardize/normalize 16 bit images with a small standard deviation

I'm trying to detect silhouettes on thermal 60x80 and depth 480x640 images by using the SSD model. I have good results on thermal images, but realy poor results on depth images. I started to ...
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How does a regression head on an R-CNN-type model know how much larger to make the region if it doesn't see the surrounding area?

From what I understand, in all of the R-CNN family of models (R-CNN, Fast R-CNN, and Faster R-CNN) there is a regression head that specifies how the bounding box should be modified from the proposed ...
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Annotation of dataset for object detection

I am annotating a dataset for object detection of UI elements (buttons, text, edit text, etc..) in images of phone screens and I am wondering what is a better approach. If I want to detect buttons (...
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Image Augmentation and Online learning

I have to question. I can't find any answers online therefore I'm going to ask them here. Is Image Augmentation in the context of Object detection always meaningful? I have 100 images of a object ...
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Are there any Non Neural Network models to do face detection in constrained domains?

In some constrained domains(eg: car driver), the camera is stationary which means the background will not change much. And we can sure when the car is running, there must be a driver. In this kind of ...
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Relationship between Average Precision and Loss for object detection/Instance segmentation

I am training a pre-trained MSCOCO Cascade R-CNN model on Cityscape dataset. While training, I do evaluation at certain steps (or periods). I noticed that at epoch 6 (training loss = 2.763) and at ...
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Is object detection the right approach for this problem

I'm trying to build a model which, given a picture of someone's face, is able to identify all the following features, as well as others. The model would output, for each picture, a list of all the ...
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On which dataset 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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What is the most comprehensive overview of methods that explain why an object detection network got a particular answer?

There are methods like Grad-CAM. With them, you can look at a particular layer of the network and see how it activated for a specific input. My question is about the methods with the purpose ...
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How to find binary image's object size like objects height,width,midpoint using python?

if you see the attached two pictures,then in "the mask" section you can see 2 binary images where white background are 0 and black marked area of object is full of values with 1 now given images like ...
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235 views

Yolov3: Single-class vs multiple class

I'm really new to object detection with Yolov3. Let's say I have 10 classes and the amount of data is approximately the same. Do I achieve better average precision when I use 10 Yolo models and train ...
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Is the following considered an image classification or an object detection problem?

I've been assigned with the task of creating a model to detect whether and advertisement exists in an image and optionally to draw a bounding box around it. My first thought was that this is an ...
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How should I find similarities between individuals with multiple measures per individual?

I want to identify individual monkeys from a camera trap survey. From my accumulated camera trap videos, I see some variation in tail tuft size that I think could help me identify monkeys. I took ...
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Retraining of object detection CNN

I am working on an object detection system that should detect UI elements (such as button, checkbox, radio button, etc..) in the photo of a touch screen of printer (not screenshots, but literally a ...
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30 views

Performance of MaskRCNN/YOLO as a function of object size in pixels

I am trying to find references on how the resolution of an object affects the ability of object detection systems such as MaskRCNN and YOLO to correctly identify the object. For example, if the ...
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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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Can we calculate mean recall and precision

I'm evaluating the accuracy in detecting objects for my image data set using three deep learning algorithms. I have selected a sample of 30 images. To measure the accuracy, I manually count the number ...
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Sample size for the evaluation of Deep Learning Models

I'm evaluating the performance and accuracy in detecting objects for my data set using three deep learning algorithms. In total there are 24,085 images. I measure the performance in terms of time ...
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What would be the ideal dataset to train a model to detect advertisements in an image?

I am thinking of the requirements for training a model that would be able to detect if there is any kind of ad in an image. I know that this sound too broad not just for a question on CV but for ...