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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22 views

Multiple labels for the same rectbox?

My goal is to identify the horse in a photo. I'm dealing with about 500 unique horses. My feeling is that the best way to distinguish one horse from another is by its face. So I trained Yolov5 ...
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10 views

Can a GIoU loss (generalized intersection over union) be used after an STN module (spatial transformer network)?

I have a model that uses an STN module for number detection and Mean Squared Error loss. But I would like to replace it for GIoU, because MSE doesn't take into account how much of the target area has ...
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17 views

How to Tensorflow models and YOLO differ in terms of training steps?

Can anybody explain how the training steps work for the Tensorflow Object Detection algorithms available on the Tensorflow 2 Detection Model Zoo? For instance, YOLOv5 cycles through epochs. As I ...
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24 views

When is an object detection approach over a CNN approach appropriate?

I understand that CNNs are for image classification while object detection is for localization + classification of the objects detected. However, in particular, AI for chest radiographs, why is object ...
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43 views

What kind of algorithm or approach can I use to find a specific type of object in an image?

What kind of algorithm or approach can I use to find a specific type of object in an image? In particular, I am interested in finding an object like a windmill in an image taken, for example, from ...
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Why the two identical "IoU comparing procedures" are needed in Faster R-CNN(RPN & RCNN)?

As far as I know, there are two same 'IoU comparing procedures' in RPN and RCNN, but why is the same operation held twice? The paragraph right below is what I've comprehended about the Faster R-CNN. ...
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1answer
93 views

How do I choose the hyper-parameters for a model to detect different guitar chords?

I need to build a hand detector that recognizes the chord played by a hand on a guitar. I read this article Static Hand Gesture Recognition using Convolutional Neural Network with Data Augmentation ...
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33 views

Which method can accurately detect circular/angular shapes? (attached example)

Is there a method to detect shapes like these accurately and efficiently? I have tried the OpenCv Haar Casacade Classifier which does not work well. These shapes should all be the same class object ...
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1answer
24 views

Why do the object detection networks produce multiple anchor boxes per location?

In various neural network detection pipelines, the detection works as follows: One processes the input image through the pretrained backbone Some additional convolutional layers The detection head, ...
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12 views

Counting number of coaches in a train from real time video feed

I have a real time video feed of a train platform. I was able to detect coaches using CNN based model. But how can I calculate number of coaches in the train that passed the platform as well as the ...
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22 views

Data Augmentation for Object Detection - Polygon Region Shape

I'm looking to run a Mask RCNN code on my dataset of about 2700 images. The images are too large and I would like to resize them, and I would also like to add some shear, scale and zoom augmentations. ...
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In anchor based object detection, why don't the anchors share the same weights?

After reading about YOLO V3 and Faster R-CNN, I don't understand why the weights for the regression head aren't the same across all boxes of the same size. Given that the backbone of these systems is ...
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Which neural network architecture to use to detect very close and very small blobs in high resolution fluorescence images?

Context I am developing a pipeline to automate the detection of small, almost circular, bright blobs (4px) (see first image below) on high-resolution fluorescence images (2048px) and later to assign ...
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1answer
70 views

Tensorflow object detection model total loss starts out good, but suddenly explodes up to high loss numbers

I'm training a Tensorflow object detection model with approx. 7500 images of two classes, which contains approx. 10,000 classes per class. I'm using Tensorflow 2.6.0, in case that is relavent. I am ...
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1answer
37 views

How to handle an unbalanced dataset when training object detection algorithms?

I am training an object detection model, and I have some very highly unbalanced data annotations. I have almost 11,000 images, all with dimensions of 1024 $\times$ 1024. Within those images I have the ...
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28 views

How big should the dataset for retraining ssd_mobilenet_v2 be?

I have retrained ssd_mobilenet_v2 using my own dataset with 2 classes (pen or pencil), using object detection API. For my project, I expect users to select specific pencils from all pencils and ...
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11 views

Role of confidence or classification score in object detection mAP metrics

I know that mAP (mean Average Precision) is the common evaluation metric for the object detection tasks. It uses IoU (Intersection over Union) threshold such as mAP@0.5 to evaluate whether the ...
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1answer
12 views

What does a value of -1.000 mean in MS COCO Metrics for Object Detection

I am training some Object-Detection-Models from the TensorFlow Object Detection API and got from the evaluation with MS COCO metrics the following results for Average Precision: IoU = 0.5;0.9 maxDets =...
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How to get bounding box coordinates of all detected objects in yolov5 object detection?

While there is a similar question on stackoverflow, it pertains to yolov4 which, unlike yolov5, uses darknet. Yolov5 is far more intuitive to use than v4 and so a solution to this in v5 is desirable. ...
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How to improve detection of wide objects?

I am working on a project where part of it is to detect PV module arrays, I trained few object detection models through TensorFlow Object Detection API and the problem I got is that the trained models ...
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1answer
88 views

Is it possible that the fine-tuned pre-trained model performs worse than the original pre-trained model?

I have downloaded a pre-trained EfficientDet D2 model (Tensorflow 2.0) and trained it on some data (about 20000 images with 20 classes). I set the number of steps to 25000 and batch size to 3 (...
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How to scale Computer Vision? How to implement Emotion detection from live video feed of N different video simultaneously?

I have a pipeline based on Scaled Yolov4 detection algorithm for faces which extract faces of users and then uses a CNN to ...
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How to work deeper with YOLO v4 scaled

I am pretty new in object detection. When I did some classification using TF with pretrained models I changed some last layers and choosed how many layers I want to train. I don't really get it with ...
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Vector input to CNN for object detection

I am training a 3D object detection network (Retinanet-based as of the moment) for re-detecting tracked objects. I would like to be able to add the velocity vector of the tracked object as an input to ...
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8 views

Pretraining on ImageNet for YOLOv1

It is mentioned in the YOLOv1 paper that the authors pretrained their network on ImageNet with a resolution of (224,224), but while training for detection tasks, they use a resolution of (448,448). ...
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Looking for advice on how to train an AI object detection algorithm to recognize smaller objects than what it has been trained on

For some academic work, I am training an AI object detection algorithm (TensorFlow models) to look for specific objects (plants, in my case). I am taking photos with a hand-held camera, and am having ...
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13 views

Object localisation and window size(can’t use learning methods). Share the resources to solve this problem

Given two images($I_1$, $I_2$) and both images contain a similar object. First, find the location of the windows which contains the object for each image. For example, let $I_{n \times n}$ is a 2-D ...
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1answer
319 views

How to add negative samples for object detection?

My question is: how to add certain negative samples to the training dataset to suppress those samples that are recognized as the object. For example, if I want to train a car detector. All my training ...
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28 views

How to change number of trained layers in object detection TensorFlow models?

Training custom object detection models with TensorFlow usually means a transfer learning of pre-trained models and, if I understand it correctly, it means only training the few last layers, with ...
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11 views

Are supervised learning object recognition models appropriate for the challenge of recognizing dynamically generated terrain features in a game?

I decided to practice applying object recognition with TensorFlow for an interesting application this weekend. The application I chose was to recognize enemies in a game world, and as more of a ...
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1answer
23 views

What to do when the ROIs are smaller than $227 \times 227$ in R-CNN?

As English is not my native language, I have some hard time understanding the following sentence: Regardless of the size or aspect ratio of the candidate region, we warp all pixels in a tight ...
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11 views

How to change a single object detection network to a multiple object detection network?

I have trained a CNN network to detect a circle and approximate its centre and radius in an image. What I want to do now is detect the centre and radius of all the circles if there are multiple ...
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How can I get the images with the highest activation for a given unit?

I am new to machine learning. I am working on the pretrained AlexNet on Pytorch and i would like to visualize the receptive fields of a given unit U. To do that I am trying to give like 200K images as ...
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10 views

Inconsistent Classification Accuracy between Classification Network & Object Detection

I have been working on an object detection and classification problem, and I am having understanding a discrepancy in my results. I am try to detect and classify 2 classes. These objects are ...
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1answer
29 views

How to reject boxes inside each other with Non Max Suppression

I’m working on an object detection cnn, and having some issues with non max suppression. When I have a small box inside a large box, NMS is not rejecting the smaller, incorrect box, because its IOU is ...
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10 views

Should we create a label mean group of small nearby objects in object detection?

I'm working on object detection models and my dataset sometimes has a lot of small objects (stay far from the scene) (overlapping and nearby) which is really annoying in annotating (it's too small and ...
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1answer
45 views

Number of classes vs number of parameters/layers?

How to estimate the number of parameters in CNN for object detection? I know that there are some well-known architectures that was trained on a lot of data (AlexNet, ResNet, VGG, GoogleLeNet). But ...
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23 views

Finding an object in a large image

I am looking for an algorithm to solve the following problem. There is a very large image (road map). A small distorted part of big image is fed into the input. I want to find the location of a small ...
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21 views

Is it possible to do object detection on an object classification dataset?

I'm new to computer vision, which I find fascinating. I wonder whether it is possible or if there has been any research into going from object recognition data to object detection. In other words, ...
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1answer
100 views

How does Mask R-CNN automatically output a different number of objects on the image?

Recently, I was reading Pytorch's official tutorial about Mask R-CNN. When I run the code on colab, it turned out that it automatically outputs a different number of channels during prediction. If the ...
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How to obtain part filter anchors in DPM Detector

The DPM detector (https://cs.brown.edu/people/pfelzens/papers/lsvm-pami.pdf) uses latent-svm to train the weights of the root and part filters. During training for positive samples, it alternates ...
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1answer
67 views

When exactly am I overfitting -- contradicting metrics

I am training an object detection machine learning pipeline. Among the many metrics provided out of the box by tensorflow object detection API, I look at total_loss and DetectionBoxes_Precision/mAP@....
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1answer
103 views

Why object detection algorithms are poor in optical character recognition?

OCR is still a very hard problem. We don't have universal powerful solutions. We use the CTC loss function An Intuitive Explanation of Connectionist Temporal Classification | Towards Data Science ...
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1answer
93 views

Object Detection: Can I modify this script to support larger images (Scaled YOLOv4)?

I am looking at training the Scaled YOLOv4 on TensorFlow 2.x, as can be found at this link. I plan to collect the imagery, annotate the objects within the image in VOC format, and then use these ...
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1answer
47 views

Viola-Jones algorithm: Haar-like features, how are the features extracted?

If I have an image like this 1 2 3 4 5 6 7 8 a b c d e f g h ... And I apply a Haar-like feature with a template ...
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Advice required for identifying bone fragments in CT-scans using STL Files (3D image segmentation)

I am working on a project related to automating the procedure of manually segmenting some bones in CT scans and hopefully if everything goes alright in this stage, move on to do something more with ...
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1answer
66 views

How can Image Caption work?

I have two models and a file contains captions for images. The output of model 1 is .pkl files that contain the features of the images. Model 2 is the language model that will be trained with the ...
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2answers
51 views

Should I use U-net to label keys in a keyboard image?

This is a 600*800 image. Which algorithm/model should I use to get an image like the one below, in which each key is detected and labeled by a rectangle? I guess this is some kind of a segmentation ...
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1answer
22 views

Why do popular object detecting models output heatmaps instead of coordinators of object directly?

I think heatmap outputs of architectures like CenterNet, OpenPose, etc. can be changed to coordinator outputs, and loss functions like focal loss can be modified so they can deal with coordinators ...
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106 views

What are the main differences between YOLOv3 and RetinaNet object detection algorithms?

I am looking at a certain project that compares performance on a certain dataset for an object detection problem using YOLOv3 and RetinaNet (or the "SSD_ResNet50_FPN" from TF Model Zoo). ...